1
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Xu G, Zhang M, Mei T, Liu W, Wang L, Xiao K. Nanofluidic Ionic Memristors. ACS NANO 2024. [PMID: 39022809 DOI: 10.1021/acsnano.4c06467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.
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Affiliation(s)
- Guoheng Xu
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
| | - Miliang Zhang
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
| | - Tingting Mei
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
| | - Wenchao Liu
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
| | - Li Wang
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
| | - Kai Xiao
- Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Advanced Biomaterials, Institute of Innovative Materials, Southern University of Science and Technology (SUSTech), Shenzhen 518055, P. R. China
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2
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Woo KS, Park H, Ghenzi N, Talin AA, Jeong T, Choi JH, Oh S, Jang YH, Han J, Williams RS, Kumar S, Hwang CS. Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing. ACS NANO 2024; 18:17007-17017. [PMID: 38952324 DOI: 10.1021/acsnano.4c03238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.
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Affiliation(s)
- Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Hyungjun Park
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Nestor Ghenzi
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Universidad de Avellaneda UNDAV and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mario Bravo 1460, Avellaneda, Buenos Aires 1872, Argentina
| | - A Alec Talin
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Taeyoung Jeong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Jung-Hae Choi
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Sangheon Oh
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - R Stanley Williams
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
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3
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Belleri P, Pons I Tarrés J, McCulloch I, Blom PWM, Kovács-Vajna ZM, Gkoupidenis P, Torricelli F. Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics. Nat Commun 2024; 15:5350. [PMID: 38914568 PMCID: PMC11196688 DOI: 10.1038/s41467-024-49668-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.
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Affiliation(s)
- Pietro Belleri
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy
| | - Judith Pons I Tarrés
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Iain McCulloch
- Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, UK
| | - Paul W M Blom
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Zsolt M Kovács-Vajna
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy
| | - Paschalis Gkoupidenis
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany.
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, USA.
- Department of Physics, North Carolina State University, 2401 Stinson Dr, Raleigh, NC, USA.
| | - Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy.
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4
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Kim G, In JH, Lee Y, Rhee H, Park W, Song H, Park J, Jeon JB, Brown TD, Talin AA, Kumar S, Kim KM. Mott neurons with dual thermal dynamics for spatiotemporal computing. NATURE MATERIALS 2024:10.1038/s41563-024-01913-0. [PMID: 38890486 DOI: 10.1038/s41563-024-01913-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 05/05/2024] [Indexed: 06/20/2024]
Abstract
Heat dissipation is a natural consequence of operating any electronic system. In nearly all computing systems, such heat is usually minimized by design and cooling. Here, we show that the temporal dynamics of internally produced heat in electronic devices can be engineered to both encode information within a single device and process information across multiple devices. In our demonstration, electronic NbOx Mott neurons, integrated on a flexible organic substrate, exhibit 18 biomimetic neuronal behaviours and frequency-based nociception within a single component by exploiting both the thermal dynamics of the Mott transition and the dynamical thermal interactions with the organic substrate. Further, multiple interconnected Mott neurons spatiotemporally communicate purely via heat, which we use for graph optimization by consuming over 106 times less energy when compared with the best digital processors. Thus, exploiting natural thermal processes in computing can lead to functionally dense, energy-efficient and radically novel mixed-physics computing primitives.
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Affiliation(s)
- Gwangmin Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Younghyun Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Juseong Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jae Bum Jeon
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | | | - A Alec Talin
- Sandia National Laboratories, Livermore, CA, USA
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, CA, USA.
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
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5
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Ren H, Li F, Wang M, Liu G, Li D, Wang R, Chen Y, Tang Y, Wang Y, Jin R, Huang Q, Xing L, Chen X, Wang J, Guo C, Zhu B. An Ion-Mediated Spiking Chemical Neuron based on Mott Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403678. [PMID: 38887824 DOI: 10.1002/adma.202403678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/31/2024] [Indexed: 06/20/2024]
Abstract
Artificial spiking neurons capable of interpreting ionic information into electrical spikes are critical to mimic biological signaling systems. Mott memristors are attractive for constructing artificial spiking neurons due to their simple structure, low energy consumption, and rich neural dynamics. However, challenges remain in achieving ion-mediated spiking and biohybrid-interfacing in Mott neurons. Here, a biomimetic spiking chemical neuron (SCN) utilizing an NbOx Mott memristor and oxide field-effect transistor-type chemical sensor is introduced. The SCN exhibits both excitation and inhibition spiking behaviors toward ionic concentrations akin to biological neural systems. It demonstrates spiking responses across physiological and pathological Na+ concentrations (1-200 × 10-3 m). The Na+-mediated SCN enables both frequency encoding and time-to-first-spike coding schemes, illustrating the rich neural dynamics of Mott neuron. In addition, the SCN interfaced with L929 cells facilitates real-time modulation of ion-mediated spiking under both normal and salty cellular microenvironments.
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Affiliation(s)
- Huihui Ren
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Fanfan Li
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Min Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Guolei Liu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Rui Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Yitong Chen
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Yingjie Tang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Yan Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Ran Jin
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Qi Huang
- Westlake Institute for Optoelectronics, Hangzhou, 311421, China
| | - Lixiang Xing
- Westlake Institute for Optoelectronics, Hangzhou, 311421, China
| | - Xiaopeng Chen
- Enovated3D (Hangzhou) Technology Development Co. Ltd., Hangzhou, 310051, China
| | - Juan Wang
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Chengchen Guo
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
- Westlake Institute for Optoelectronics, Hangzhou, 311421, China
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6
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Gaggio B, Jan A, Muller M, Salonikidou B, Bakhit B, Hellenbrand M, Di Martino G, Yildiz B, MacManus-Driscoll JL. Sodium-Controlled Interfacial Resistive Switching in Thin Film Niobium Oxide for Neuromorphic Applications. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2024; 36:5764-5774. [PMID: 38883429 PMCID: PMC11170940 DOI: 10.1021/acs.chemmater.4c00965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/24/2024] [Accepted: 05/24/2024] [Indexed: 06/18/2024]
Abstract
A double layer 2-terminal device is employed to show Na-controlled interfacial resistive switching and neuromorphic behavior. The bilayer is based on interfacing biocompatible NaNbO3 and Nb2O5, which allows the reversible uptake of Na+ in the Nb2O5 layer. We demonstrate voltage-controlled interfacial barrier tuning via Na+ transfer, enabling conductivity modulation and spike-amplitude- and spike-timing-dependent plasticity. The neuromorphic behavior controlled by Na+ ion dynamics in biocompatible materials shows potential for future low-power sensing electronics and smart wearables with local processing.
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Affiliation(s)
- Benedetta Gaggio
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Atif Jan
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Moritz Muller
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Barbara Salonikidou
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Babak Bakhit
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
- Electrical Engineering Division, Department of Engineering, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FA, U.K
- Thin Film Physics Division, Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping SE-581 83, Sweden
| | - Markus Hellenbrand
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Giuliana Di Martino
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Bilge Yildiz
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Judith L MacManus-Driscoll
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
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7
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Xiong J, Xie J, Cheng B, Dai Y, Cui X, Wang L, Liu Z, Zhou J, Wang N, Xu X, Chen X, Cheong SW, Liang SJ, Miao F. Electrical switching of Ising-superconducting nonreciprocity for quantum neuronal transistor. Nat Commun 2024; 15:4953. [PMID: 38858363 PMCID: PMC11164936 DOI: 10.1038/s41467-024-48882-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 05/13/2024] [Indexed: 06/12/2024] Open
Abstract
Nonreciprocal quantum transport effect is mainly governed by the symmetry breaking of the material systems and is gaining extensive attention in condensed matter physics. Realizing electrical switching of the polarity of the nonreciprocal transport without external magnetic field is essential to the development of nonreciprocal quantum devices. However, electrical switching of superconducting nonreciprocity remains yet to be achieved. Here, we report the observation of field-free electrical switching of nonreciprocal Ising superconductivity in Fe3GeTe2/NbSe2 van der Waals (vdW) heterostructure. By taking advantage of this electrically switchable superconducting nonreciprocity, we demonstrate a proof-of-concept nonreciprocal quantum neuronal transistor, which allows for implementing the XOR logic gate and faithfully emulating biological functionality of a cortical neuron in the brain. Our work provides a promising pathway to realize field-free and electrically switchable nonreciprocity of quantum transport and demonstrate its potential in exploring neuromorphic quantum devices with both functionality and performance beyond the traditional devices.
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Affiliation(s)
- Junlin Xiong
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Jiao Xie
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Bin Cheng
- Institute of Interdisciplinary Physical Sciences, School of Science, Nanjing University of Science and Technology, 210094, Nanjing, China.
| | - Yudi Dai
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Xinyu Cui
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Lizheng Wang
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Zenglin Liu
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Ji Zhou
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China
| | - Naizhou Wang
- Hefei National Laboratory for Physical Science at Microscale and Department of Physics and Key Laboratory of Strongly Coupled Quantum Matter Physics, University of Science and Technology of China, 230026, Hefei, Anhui, China
| | - Xianghan Xu
- Center for Quantum Materials Synthesis and Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Xianhui Chen
- Hefei National Laboratory for Physical Science at Microscale and Department of Physics and Key Laboratory of Strongly Coupled Quantum Matter Physics, University of Science and Technology of China, 230026, Hefei, Anhui, China
| | - Sang-Wook Cheong
- Center for Quantum Materials Synthesis and Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Shi-Jun Liang
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China.
| | - Feng Miao
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, 210093, Nanjing, China.
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8
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Bae J, Kwon C, Park SO, Jeong H, Park T, Jang T, Cho Y, Kim S, Choi S. Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems. SCIENCE ADVANCES 2024; 10:eadm7221. [PMID: 38848362 PMCID: PMC11160469 DOI: 10.1126/sciadv.adm7221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO2-x memristors, we experimentally demonstrate reduced device variations, improved switching speeds, and enhanced switching windows. Atomistic simulations of amorphous TiO2-x reveal that fluoride ions attract oxygen vacancies, improving the reversible redistribution and uniformity. A number of migration barrier calculations statistically show that fluoride ions also reduce the migration energies of nearby oxygen vacancies, facilitating ionic diffusion and high-speed switching. The detailed Voronoi volume analysis further suggests design principles in terms of the migrating species' electrostatic repulsion and migration barriers. This work presents an innovative methodology for the fabrication of reliable memristor devices, contributing to the realization of hardware-based neuromorphic systems.
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Affiliation(s)
- Jongmin Bae
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Choah Kwon
- Department of Nuclear Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - See-On Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hakcheon Jeong
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Taehoon Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Taehwan Jang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yoonho Cho
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Sangtae Kim
- Department of Nuclear Engineering, Hanyang University, Seoul 04763, Republic of Korea
- Department of Material Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Shinhyun Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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9
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Nath SK, Das SK, Nandi SK, Xi C, Marquez CV, Rúa A, Uenuma M, Wang Z, Zhang S, Zhu RJ, Eshraghian J, Sun X, Lu T, Bian Y, Syed N, Pan W, Wang H, Lei W, Fu L, Faraone L, Liu Y, Elliman RG. Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2400904. [PMID: 38516720 DOI: 10.1002/adma.202400904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/18/2024] [Indexed: 03/23/2024]
Abstract
The application of hardware-based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V3O5. The devices exhibit electroforming-free operation with switching parameters that can be tuned by optical illumination. Using temperature-dependent electrical measurements, conductive atomic force microscopy (C-AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V3O5, which eventually affects the oscillation dynamics. Furthermore, V3O5 is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as -4.6% K-1 at 423 K. The utility of these devices is illustrated by demonstrating in-sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices.
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Affiliation(s)
- Shimul Kanti Nath
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
- School of Photovoltaic and Renewable Energy Engineering, University of New South Wales (UNSW Sydney), Kensington, NSW, 2052, Australia
| | - Sujan Kumar Das
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
- Department of Physics, Jahangirnagar Univeristy, Savar, Dhaka, 1342, Bangladesh
| | - Sanjoy Kumar Nandi
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
| | - Chen Xi
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Pok Fu Lam Rd, Hong Kong Island, Hong Kong
| | | | - Armando Rúa
- Department of Physics, University of Puerto Rico, Mayaguez, PR, 00681, USA
| | - Mutsunori Uenuma
- Information Device Science Laboratory, Nara Institute of Science and Technology (NAIST), Nara, 630-0192, Japan
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Pok Fu Lam Rd, Hong Kong Island, Hong Kong
| | - Songqing Zhang
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Rui-Jie Zhu
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA
| | - Jason Eshraghian
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA
| | - Xiao Sun
- John de Laeter Centre, Curtin University, Perth, WA, 6102, Australia
| | - Teng Lu
- Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia
| | - Yue Bian
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
- Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, Canberra, ACT, 2601, Australia
| | - Nitu Syed
- Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, School of Physics, University of Melbourne, Melbourne, Victoria, 3010, Australia
| | - Wenwu Pan
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
- Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, Perth, WA, 6009, Australia
| | - Han Wang
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Wen Lei
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Lan Fu
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
- Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, Canberra, ACT, 2601, Australia
| | - Lorenzo Faraone
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
- Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems, Perth, WA, 6009, Australia
| | - Yun Liu
- Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia
| | - Robert G Elliman
- Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia
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10
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Woo KS, Zhang A, Arabelo A, Brown TD, Park M, Talin AA, Fuller EJ, Bisht RS, Qian X, Arroyave R, Ramanathan S, Thomas L, Williams RS, Kumar S. True random number generation using the spin crossover in LaCoO 3. Nat Commun 2024; 15:4656. [PMID: 38821970 PMCID: PMC11143320 DOI: 10.1038/s41467-024-49149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/23/2024] [Indexed: 06/02/2024] Open
Abstract
While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.
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Affiliation(s)
- Kyung Seok Woo
- Sandia National Laboratories, Livermore, CA, USA
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Alan Zhang
- Sandia National Laboratories, Livermore, CA, USA
| | - Allison Arabelo
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Minseong Park
- Sandia National Laboratories, Livermore, CA, USA
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - A Alec Talin
- Sandia National Laboratories, Livermore, CA, USA
| | | | - Ravindra Singh Bisht
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Xiaofeng Qian
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Raymundo Arroyave
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Shriram Ramanathan
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Luke Thomas
- Applied Materials Inc., Santa Clara, CA, USA
| | - R Stanley Williams
- Sandia National Laboratories, Livermore, CA, USA.
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, CA, USA.
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11
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van Essen PJ, Nie Z, de Keijzer B, Kraus PM. Toward Complete All-Optical Intensity Modulation of High-Harmonic Generation from Solids. ACS PHOTONICS 2024; 11:1832-1843. [PMID: 38766500 PMCID: PMC11100285 DOI: 10.1021/acsphotonics.4c00156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/22/2024]
Abstract
Optical modulation of high-harmonics generation in solids enables the detection of material properties, such as the band structure, and promising new applications, such as super-resolution imaging in semiconductors. Various recent studies have shown optical modulation of high-harmonics generation in solids, in particular, suppression of high-harmonics generation has been observed by synchronized or delayed multipulse sequences. Here we provide an overview of the underlying mechanisms attributed to this suppression and provide a perspective on the challenges and opportunities regarding these mechanisms. All-optical control of high-harmonic generation allows for femtosecond, and in the future possibly subfemtosecond, switching, which has numerous possible applications: These range from super-resolution microscopy to nanoscale controlled chemistry and highly tunable nonlinear light sources.
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Affiliation(s)
- Pieter J. van Essen
- Advanced
Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - Zhonghui Nie
- Advanced
Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - Brian de Keijzer
- Advanced
Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - Peter M. Kraus
- Advanced
Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
- Department
of Physics and Astronomy, and LaserLaB, Vrije Universiteit, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
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12
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Li F, Li D, Wang C, Liu G, Wang R, Ren H, Tang Y, Wang Y, Chen Y, Liang K, Huang Q, Sawan M, Qiu M, Wang H, Zhu B. An artificial visual neuron with multiplexed rate and time-to-first-spike coding. Nat Commun 2024; 15:3689. [PMID: 38693165 PMCID: PMC11063071 DOI: 10.1038/s41467-024-48103-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
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Affiliation(s)
- Fanfan Li
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Chuanqing Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
| | - Guolei Liu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yingjie Tang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yan Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yitong Chen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Kun Liang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Qi Huang
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Min Qiu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China.
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China.
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China.
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China.
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13
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Fida AA, Mittal S, Khanday FA. Mott memristor based stochastic neurons for probabilistic computing. NANOTECHNOLOGY 2024; 35:295201. [PMID: 38593756 DOI: 10.1088/1361-6528/ad3c4b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 04/09/2024] [Indexed: 04/11/2024]
Abstract
Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor's inherent stochastic switching dynamics. We demonstrate that the developed LIF neuron is capable of biological neural dynamics. We leverage these characteristics of the proposed LIF neuron by integrating it into a population-coded spiking neural network and a spiking restricted Boltzmann machine (sRBM), thereby showcasing its ability to implement probabilistic learning and inference. The sRBM achieves a software-comparable accuracy of 87.13%. Unlike CMOS-based probabilistic neurons, our design does not require any external noise sources. The designed neurons are highly energy efficient and ultra-compact, requiring only three components: a resistor, a capacitor and a memristor device.
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Affiliation(s)
- Aabid Amin Fida
- Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, Uttrakhand, India
| | - Sparsh Mittal
- Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, Uttrakhand, India
| | - Farooq Ahmad Khanday
- Electronics and Instrumentation Technology, University of Kashmir, Srinagar, J&K, India
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14
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Xiang Z, Zheng JY, Ma X, Chu Y, Song Q, Zhou G, Zou B, Wu H, Wang C. FEN1-assisted DNA logic amplifier circuit for fast and compact DNA computing. Chem Commun (Camb) 2024; 60:4593-4596. [PMID: 38577866 DOI: 10.1039/d4cc00203b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
This work developed DNA amplifier logic gates (AND-OR, OR-AND, FAN-IN, FAN-OUT, and 4-bit square-root circuits) using a flap endonuclease 1 (FEN1)-catalyzed signal amplification reaction, for the fastest and compact DNA computing. Moreover, the logic circuit can use input strands with concentrations of less than 1 nM, which is more than 100 times lower than the input concentration of other DNA logic circuits, providing a promising methodology for constructing fast and compact DNA computations.
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Affiliation(s)
- Zheng Xiang
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jia-Yi Zheng
- Key Laboratory of Drug Quality Control and Pharmacovigilance of Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
| | - Xueping Ma
- Department of Clinical Pharmacy, State Key Laboratory of Analytical Chemistry for Life Science and Jiangsu Key Laboratory of Molecular Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Yanan Chu
- Department of Clinical Pharmacy, State Key Laboratory of Analytical Chemistry for Life Science and Jiangsu Key Laboratory of Molecular Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Qinxin Song
- Key Laboratory of Drug Quality Control and Pharmacovigilance of Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
| | - Guohua Zhou
- Department of Clinical Pharmacy, State Key Laboratory of Analytical Chemistry for Life Science and Jiangsu Key Laboratory of Molecular Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Bingjie Zou
- Key Laboratory of Drug Quality Control and Pharmacovigilance of Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
| | - Haiping Wu
- Department of Clinical Pharmacy, State Key Laboratory of Analytical Chemistry for Life Science and Jiangsu Key Laboratory of Molecular Medicine, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
| | - Chen Wang
- Key Laboratory of Drug Quality Control and Pharmacovigilance of Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
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15
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Chen X, Yang D, Hwang G, Dong Y, Cui B, Wang D, Chen H, Lin N, Zhang W, Li H, Shao R, Lin P, Hong H, Yao Y, Sun L, Wang Z, Yang H. Oscillatory Neural Network-Based Ising Machine Using 2D Memristors. ACS NANO 2024; 18:10758-10767. [PMID: 38598699 DOI: 10.1021/acsnano.3c10559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.
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Affiliation(s)
- Xi Chen
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dongliang Yang
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Geunwoo Hwang
- Division of Chemical Engineering and Materials Science, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Yujiao Dong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Binbin Cui
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dingchen Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Hegan Chen
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ning Lin
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Wenqi Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Huihan Li
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Ruiwen Shao
- Beijing Advanced Innovation Center for Intelligent Robots and Systems and Institute of Engineering Medicine, Beijing Institute of Technology, Beijing 100081, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Hang Zhou 310013, China
| | - Heemyoung Hong
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Yugui Yao
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Linfeng Sun
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Heejun Yang
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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16
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Yang K, Wang Y, Tiw PJ, Wang C, Zou X, Yuan R, Liu C, Li G, Ge C, Wu S, Zhang T, Huang R, Yang Y. High-order sensory processing nanocircuit based on coupled VO 2 oscillators. Nat Commun 2024; 15:1693. [PMID: 38402226 PMCID: PMC10894221 DOI: 10.1038/s41467-024-45992-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 02/08/2024] [Indexed: 02/26/2024] Open
Abstract
Conventional circuit elements are constrained by limitations in area and power efficiency at processing physical signals. Recently, researchers have delved into high-order dynamics and coupled oscillation dynamics utilizing Mott devices, revealing potent nonlinear computing capabilities. However, the intricate yet manageable population dynamics of multiple artificial sensory neurons with spatiotemporal coupling remain unexplored. Here, we present an experimental hardware demonstration featuring a capacitance-coupled VO2 phase-change oscillatory network. This network serves as a continuous-time dynamic system for sensory pre-processing and encodes information in phase differences. Besides, a decision-making module for special post-processing through software simulation is designed to complete a bio-inspired dynamic sensory system. Our experiments provide compelling evidence that this transistor-free coupling network excels in sensory processing tasks such as touch recognition and gesture recognition, achieving significant advantages of fewer devices and lower energy-delay-product compared to conventional methods. This work paves the way towards an efficient and compact neuromorphic sensory system based on nano-scale nonlinear dynamics.
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Affiliation(s)
- Ke Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yanghao Wang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Pek Jun Tiw
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Chaoming Wang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Xiaolong Zou
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Rui Yuan
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Ge Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Si Wu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Teng Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
| | - Ru Huang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yuchao Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China.
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17
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Lee YJ, Kim Y, Gim H, Hong K, Jang HW. Nanoelectronics Using Metal-Insulator Transition. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305353. [PMID: 37594405 DOI: 10.1002/adma.202305353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/02/2023] [Indexed: 08/19/2023]
Abstract
Metal-insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive change in Mott insulators has attracted tremendous interest for investigation into next-generation electronic and optoelectronic devices, as well as a fundamental understanding of condensed matter systems. Although the mechanism of MIT in Mott insulators is still controversial, great efforts have been made to understand and modulate MIT behavior for various electronic and optoelectronic applications. In this review, recent progress in the field of nanoelectronics utilizing MIT is highlighted. A brief introduction to the physics of MIT and its underlying mechanisms is begun. After discussing the MIT behaviors of various Mott insulators, recent advances in the design and fabrication of nanoelectronics devices based on MIT, including memories, gas sensors, photodetectors, logic circuits, and artificial neural networks are described. Finally, an outlook on the development and future applications of nanoelectronics utilizing MIT is provided. This review can serve as an overview and a comprehensive understanding of the design of MIT-based nanoelectronics for future electronic and optoelectronic devices.
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Affiliation(s)
- Yoon Jung Lee
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Youngmin Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyeongyu Gim
- Department of Materials Science and Engineering, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Kootak Hong
- Department of Materials Science and Engineering, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea
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18
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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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19
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Nie Z, Guery L, Molinero EB, Juergens P, van den Hooven TJ, Wang Y, Jimenez Galan A, Planken PCM, Silva REF, Kraus PM. Following the Nonthermal Phase Transition in Niobium Dioxide by Time-Resolved Harmonic Spectroscopy. PHYSICAL REVIEW LETTERS 2023; 131:243201. [PMID: 38181131 DOI: 10.1103/physrevlett.131.243201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/30/2023] [Accepted: 10/26/2023] [Indexed: 01/07/2024]
Abstract
Photoinduced phase transitions in correlated materials promise diverse applications from ultrafast switches to optoelectronics. Resolving those transitions and possible metastable phases temporally are key enablers for these applications, but challenge existing experimental approaches. Extreme nonlinear optics can help probe phase changes, as higher-order nonlinearities have higher sensitivity and temporal resolution to band structure and lattice deformations. Here the ultrafast transition from the semiconducting to the metallic phases in polycrystalline thin-film NbO_{2} is investigated by time-resolved harmonic spectroscopy. The emission strength of all harmonic orders shows a steplike suppression when the excitation fluence exceeds a threshold (∼11-12 mJ/cm^{2}), below the fluence required for the thermal transition-a signature of the nonthermal emergence of a metallic phase within 100±20 fs. This observation is backed by full ab initio simulations as well as a 1D chain model of high-harmonic generation from both phases. Our results demonstrate femtosecond harmonic probing of phase transitions and nonthermal dynamics in solids.
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Affiliation(s)
- Z Nie
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - L Guery
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - E B Molinero
- Instituto de Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (ICMM-CSIC), E-28049 Madrid, Spain
| | - P Juergens
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
- Max-Born-Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin, Germany
| | - T J van den Hooven
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
| | - Y Wang
- School of Physics and Electronic Engineering, Taishan University 525 Dongyue Street, Tai'an, Shandong, China
| | - A Jimenez Galan
- Max-Born-Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin, Germany
| | - P C M Planken
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
- Van der Waals-Zeeman Institute, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - R E F Silva
- Instituto de Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (ICMM-CSIC), E-28049 Madrid, Spain
- Max-Born-Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin, Germany
| | - P M Kraus
- Advanced Research Center for Nanolithography, Science Park 106, 1098 XG Amsterdam, The Netherlands
- Department of Physics and Astronomy, and LaserLaB, Vrije Universiteit, De Boelelaan 1105,1081 HV Amsterdam, The Netherlands
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20
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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21
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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22
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Rhee H, Kim G, Song H, Park W, Kim DH, In JH, Lee Y, Kim KM. Probabilistic computing with NbO x metal-insulator transition-based self-oscillatory pbit. Nat Commun 2023; 14:7199. [PMID: 37938550 PMCID: PMC10632392 DOI: 10.1038/s41467-023-43085-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and high-performance probabilistic computing.
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Affiliation(s)
- Hakseung Rhee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Do Hoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Younghyun Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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23
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Han J, Lee J, Kim Y, Kim YB, Yun S, Lee S, Yu J, Lee KJ, Myung H, Choi Y. 3D Neuromorphic Hardware with Single Thin-Film Transistor Synapses Over Single Thin-Body Transistor Neurons by Monolithic Vertical Integration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302380. [PMID: 37712147 PMCID: PMC10602577 DOI: 10.1002/advs.202302380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/24/2023] [Indexed: 09/16/2023]
Abstract
Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency for artificial intelligence (AI) functions owing to its event-driven and spatiotemporally sparse operations. However, an artificial neuron and synapse based on complex complementary metal-oxide-semiconductor (CMOS) circuits limit the scalability and energy efficiency of neuromorphic hardware. In this work, a neuromorphic module is demonstrated composed of synapses over neurons realized by monolithic vertical integration. The synapse at top is a single thin-film transistor (1TFT-synapse) made of poly-crystalline silicon film and the neuron at bottom is another single transistor (1T-neuron) made of single-crystalline silicon. Excimer laser annealing (ELA) is applied to activate dopants for the 1TFT-synapse at the top and rapid thermal annealing (RTA) is applied to do so for the 1T-neuron at the bottom. Internal electro-thermal annealing (ETA) via the generation of Joule heat is also used to enhance the endurance of the 1TFT-synapse without transferring heat to the 1T-neuron at the bottom. As neuromorphic vision sensing, classification of American Sign Language (ASL) is conducted with the fabricated neuromorphic module. Its classification accuracy on ASL is ≈92.3% even after 204 800 update pulses.
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Affiliation(s)
- Joon‐Kyu Han
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jung‐Woo Lee
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
- SK Hynix Inc.Icheon17336Republic of Korea
| | - Yeeun Kim
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Young Bin Kim
- Department of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seong‐Yun Yun
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sang‐Won Lee
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Ji‐Man Yu
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Keon Jae Lee
- Department of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Hyun Myung
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Yang‐Kyu Choi
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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24
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Jiang M, Shan K, He C, Li C. Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar. Nat Commun 2023; 14:5927. [PMID: 37739944 PMCID: PMC10516914 DOI: 10.1038/s41467-023-41647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023] Open
Abstract
Combinatorial optimization problems are prevalent in various fields, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines, particularly memristor-based analog Ising machines, have emerged as promising solutions. However, existing simulate-annealing-based implementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks, including unweighted and weighted Max-Cut and traveling salesman problem, using our integrated memristor chip. The quantum-inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy-efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism, analog conductance states, and all-to-all connection provided by memristor technology, promising its potential for solving complex optimization problems with greater efficiency.
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Affiliation(s)
- Mingrui Jiang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Keyi Shan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chengping He
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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25
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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26
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Brown TD, Bohaichuk SM, Islam M, Kumar S, Pop E, Williams RS. Electro-Thermal Characterization of Dynamical VO 2 Memristors via Local Activity Modeling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205451. [PMID: 36165218 DOI: 10.1002/adma.202205451] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.
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Affiliation(s)
- Timothy D Brown
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA
- Sandia National Laboratories, Livermore, CA, 94550, USA
| | | | - Mahnaz Islam
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, CA, 94550, USA
| | - Eric Pop
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA
| | - R Stanley Williams
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA
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27
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Syed GS, Zhou Y, Warner J, Bhaskaran H. Atomically thin optomemristive feedback neurons. NATURE NANOTECHNOLOGY 2023; 18:1036-1043. [PMID: 37142710 DOI: 10.1038/s41565-023-01391-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 03/24/2023] [Indexed: 05/06/2023]
Abstract
Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibitory modulation effects. In neuromorphic computing, neurons that combine and broadcast both excitory and inhibitory signals using one nanoscale device are still an elusive goal. Here we introduce a type-II, two-dimensional heterojunction-based optomemristive neuron, using a stack of MoS2, WS2 and graphene that demonstrates both of these effects via optoelectronic charge-trapping mechanisms. We show that such neurons provide a nonlinear and rectified integration of information, that can be optically broadcast. Such a neuron has applications in machine learning, particularly in winner-take-all networks. We then apply such networks to simulations to establish unsupervised competitive learning for data partitioning, as well as cooperative learning in solving combinatorial optimization problems.
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Affiliation(s)
- Ghazi Sarwat Syed
- IBM Research - Europe, Rüschlikon, Switzerland.
- Department of Materials, University of Oxford, Oxford, UK.
| | - Yingqiu Zhou
- Department of Materials, University of Oxford, Oxford, UK
- Denmark Technical University, Lyngby, Denmark
| | - Jamie Warner
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
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28
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Talin AA, Li Y, Robinson DA, Fuller EJ, Kumar S. ECRAM Materials, Devices, Circuits and Architectures: A Perspective. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204771. [PMID: 36354177 DOI: 10.1002/adma.202204771] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/09/2022] [Indexed: 06/16/2023]
Abstract
Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.
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Affiliation(s)
- A Alec Talin
- Sandia National Laboratories, Livermore, CA, 94551, USA
| | - Yiyang Li
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | | | - Suhas Kumar
- Sandia National Laboratories, Livermore, CA, 94551, USA
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29
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Chen S, Zhang T, Tappertzhofen S, Yang Y, Valov I. Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301924. [PMID: 37199224 DOI: 10.1002/adma.202301924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.
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Affiliation(s)
- Shaochuan Chen
- Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Stefan Tappertzhofen
- Chair for Micro- and Nanoelectronics, Department of Electrical Engineering and Information Technology, TU Dortmund University, Martin-Schmeisser-Weg 4-6, D-44227, Dortmund, Germany
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China
| | - Ilia Valov
- Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
- Institute of Electrochemistry and Energy Systems "Acad. E. Budewski", Bulgarian Academy of Sciences, Acad. G. Bonchev 10, 1113, Sofia, Bulgaria
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30
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Schofield P, Bradicich A, Gurrola RM, Zhang Y, Brown TD, Pharr M, Shamberger PJ, Banerjee S. Harnessing the Metal-Insulator Transition of VO 2 in Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205294. [PMID: 36036767 DOI: 10.1002/adma.202205294] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Future-generation neuromorphic computing seeks to overcome the limitations of von Neumann architectures by colocating logic and memory functions, thereby emulating the function of neurons and synapses in the human brain. Despite remarkable demonstrations of high-fidelity neuronal emulation, the predictive design of neuromorphic circuits starting from knowledge of material transformations remains challenging. VO2 is an attractive candidate since it manifests a near-room-temperature, discontinuous, and hysteretic metal-insulator transition. The transition provides a nonlinear dynamical response to input signals, as needed to construct neuronal circuit elements. Strategies for tuning the transformation characteristics of VO2 based on modification of material properties, interfacial structure, and field couplings, are discussed. Dynamical modulation of transformation characteristics through in situ processing is discussed as a means of imbuing synaptic function. Mechanistic understanding of site-selective modification; external, epitaxial, and chemical strain; defect dynamics; and interfacial field coupling in modifying local atomistic structure, the implications therein for electronic structure, and ultimately, the tuning of transformation characteristics, is emphasized. Opportunities are highlighted for inverse design and for using design principles related to thermodynamics and kinetics of electronic transitions learned from VO2 to inform the design of new Mott materials, as well as to go beyond energy-efficient computation to manifest intelligence.
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Affiliation(s)
- Parker Schofield
- Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Adelaide Bradicich
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Rebeca M Gurrola
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Yuwei Zhang
- Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Matt Pharr
- Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Patrick J Shamberger
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Sarbajit Banerjee
- Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
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31
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Yi SI, Rath SP, Deepak, Venkatesan T, Bhat N, Goswami S, Williams RS, Goswami S. Energy and Space Efficient Parallel Adder Using Molecular Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2206128. [PMID: 36314389 DOI: 10.1002/adma.202206128] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/13/2022] [Indexed: 06/16/2023]
Abstract
A breakthrough in in-memory computing technologies hinges on the development of appropriate material platforms that can overcome their existing limitations, such as larger than optimal footprint and multiple serial computational steps, with potential accumulation of errors. Using a molecular switching element with multiple non-monotonic and deterministic transitions, the device count and the number of computational steps can be substantially reduced. With molecular materials, however, the realization of a reliable and robust platform is an unattained goal for decades. Here, crossbar arrays with up to 64 molecular memristors are fabricated to experimentally demonstrate 8-bit serial and 4-bit parallel adders that operate for thousands of measurement cycles with an estimated error probability of 10-16 . For performance benchmarking, a 32-bit parallel adder is designed and simulated with 268 million inputs including contributions from the peripheral circuitry showing a 47× higher energy efficiency, 93× faster operation, and 9% of the footprint, leading to 4390 times improved energy-delay product compared to a special purpose complementary metal-oxide-semiconductor (CMOS)-based multicore adder.
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Affiliation(s)
- Su-In Yi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 3127, USA
| | - Santi Prasad Rath
- Centre for Nanoscience and Engineering, CeNSE, Indian Institute of Science (IISc), Bangalore, 560012, India
| | - Deepak
- Centre for Nanoscience and Engineering, CeNSE, Indian Institute of Science (IISc), Bangalore, 560012, India
| | - T Venkatesan
- Center for Quantum Research and Technology (CQRT), University of Oklahoma, Norman, OK, 73019, USA
| | - Navakanta Bhat
- Centre for Nanoscience and Engineering, CeNSE, Indian Institute of Science (IISc), Bangalore, 560012, India
| | - Sreebrata Goswami
- Centre for Nanoscience and Engineering, CeNSE, Indian Institute of Science (IISc), Bangalore, 560012, India
| | - R Stanley Williams
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 3127, USA
| | - Sreetosh Goswami
- Centre for Nanoscience and Engineering, CeNSE, Indian Institute of Science (IISc), Bangalore, 560012, India
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32
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Zhou X, Zhao L, Yan C, Zhen W, Lin Y, Li L, Du G, Lu L, Zhang ST, Lu Z, Li D. Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications. Nat Commun 2023; 14:3285. [PMID: 37280223 DOI: 10.1038/s41467-023-39033-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 05/25/2023] [Indexed: 06/08/2023] Open
Abstract
As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO2-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.
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Affiliation(s)
- Xi Zhou
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Liang Zhao
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China.
- Hefei Reliance Memory Ltd., Bldg. F4-11F, Innovation Industrial Park Phase II, 230088, Hefei, China.
| | - Chu Yan
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China
| | - Weili Zhen
- High Magnetic Field Laboratory, Chinese Academy of Sciences, 230031, Hefei, China
| | - Yinyue Lin
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Le Li
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Guanlin Du
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Linfeng Lu
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Shan-Ting Zhang
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
- Zhangjiang Laboratory, 100 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
| | - Zhichao Lu
- Hefei Reliance Memory Ltd., Bldg. F4-11F, Innovation Industrial Park Phase II, 230088, Hefei, China
| | - Dongdong Li
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China.
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China.
- Zhangjiang Laboratory, 100 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China.
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33
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Leonetti G, Fretto M, Bejtka K, Olivetti ES, Pirri FC, De Leo N, Valov I, Milano G. Resistive switching and role of interfaces in memristive devices based on amorphous NbO x grown by anodic oxidation. Phys Chem Chem Phys 2023; 25:14766-14777. [PMID: 37145117 DOI: 10.1039/d3cp01160g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Memristive devices based on the resistive switching mechanism are continuously attracting attention in the framework of neuromorphic computing and next-generation memory devices. Here, we report on a comprehensive analysis of the resistive switching properties of amorphous NbOx grown by anodic oxidation. Besides a detailed chemical, structural and morphological analysis of the involved materials and interfaces, the mechanism of switching in Nb/NbOx/Au resistive switching cells is discussed by investigating the role of metal-metal oxide interfaces in regulating electronic and ionic transport mechanisms. The resistive switching was found to be related to the formation/rupture of conductive nanofilaments in the NbOx layer under the action of an applied electric field, facilitated by the presence of an oxygen scavenger layer at the Nb/NbOx interface. Electrical characterization including device-to-device variability revealed an endurance >103 full-sweep cycles, retention >104 s, and multilevel capabilities. Furthermore, the observation of quantized conductance supports the physical mechanism of switching based on the formation of atomic-scale conductive filaments. Besides providing new insights into the switching properties of NbOx, this work also highlights the perspective of anodic oxidation as a promising method for the realization of resistive switching cells.
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Affiliation(s)
- Giuseppe Leonetti
- Politecnico di Torino, Department of Applied Science and Technology (DISAT), C.so Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Matteo Fretto
- Istituto Nazionale di Ricerca Metrologica (INRiM), Advanced Materials Metrology and Life Science, Strada delle cacce 91, 10135 Turin, Italy.
| | - Katarzyna Bejtka
- Politecnico di Torino, Department of Applied Science and Technology (DISAT), C.so Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Elena Sonia Olivetti
- Istituto Nazionale di Ricerca Metrologica (INRiM), Advanced Materials Metrology and Life Science, Strada delle cacce 91, 10135 Turin, Italy.
| | - Fabrizio Candido Pirri
- Politecnico di Torino, Department of Applied Science and Technology (DISAT), C.so Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Natascia De Leo
- Istituto Nazionale di Ricerca Metrologica (INRiM), Advanced Materials Metrology and Life Science, Strada delle cacce 91, 10135 Turin, Italy.
| | - Ilia Valov
- Juelich, Institute of Electrochemistry and Energy System, Germany
- Acad. Evgeni Budevski (IEE-BAS, Bulgarian Academy of Sciences (BAS), Acad. G. Bonchev Str., Block 10, 1113 Sofia, Bulgaria
| | - Gianluca Milano
- Istituto Nazionale di Ricerca Metrologica (INRiM), Advanced Materials Metrology and Life Science, Strada delle cacce 91, 10135 Turin, Italy.
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34
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Rehman S, Khan MA, Kim H, Patil H, Aziz J, Kadam KD, Rehman MA, Rabeel M, Hao A, Khan K, Kim S, Eom J, Kim DK, Khan MF. Optically Reconfigurable Complementary Logic Gates Enabled by Bipolar Photoresponse in Gallium Selenide Memtransistor. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2205383. [PMID: 37076923 DOI: 10.1002/advs.202205383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 03/01/2023] [Indexed: 05/03/2023]
Abstract
To avoid the complexity of the circuit for in-memory computing, simultaneous execution of multiple logic gates (OR, AND, NOR, and NAND) and memory behavior are demonstrated in a single device of oxygen plasma-treated gallium selenide (GaSe) memtransistor. Resistive switching behavior with RON /ROFF ratio in the range of 104 to 106 is obtained depending on the channel length (150 to 1600 nm). Oxygen plasma treatment on GaSe film created shallow and deep-level defect states, which exhibit carriers trapping/de-trapping, that lead to negative and positive photoconductance at positive and negative gate voltages, respectively. This distinguishing feature of gate-dependent transition of negative to positive photoconductance encourages the execution of four logic gates in the single memory device, which is elusive in conventional memtransistor. Additionally, it is feasible to reversibly switch between two logic gates by just adjusting the gate voltages, e.g., NAND/NOR and AND/NAND. All logic gates presented high stability. Additionally, memtransistor array (1×8) is fabricated and programmed into binary bits representing ASCII (American Standard Code for Information Interchange) code for the uppercase letter "N". This facile device configuration can provide the functionality of both logic and memory devices for emerging neuromorphic computing.
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Affiliation(s)
- Shania Rehman
- Department of Semiconductor System Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Muhammad Asghar Khan
- Department of Physics & Astronomy and Graphene Research Institute, Sejong University, Seoul, 05006, Republic of Korea
| | - Honggyun Kim
- Department of Semiconductor System Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Harshada Patil
- Department of Electrical Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Jamal Aziz
- Department of Electrical Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Kalyani D Kadam
- Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, South Korea
| | - Malik Abdul Rehman
- Department of Chemical Engineering, New Uzbekistan University, Tashkent, 100007, Uzbekistan
| | - Muhammad Rabeel
- Department of Electrical Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Aize Hao
- State Key Laboratory of Chemistry and Utilization of Carbon-Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi, Xinjiang, 830017, P. R. China
| | - Karim Khan
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, P. R. China
| | - Sungho Kim
- Department of Semiconductor System Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Jonghwa Eom
- Department of Physics & Astronomy and Graphene Research Institute, Sejong University, Seoul, 05006, Republic of Korea
| | - Deok-Kee Kim
- Department of Semiconductor System Engineering, Sejong University, Seoul, 05006, Republic of Korea
- Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, South Korea
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, 05006, Republic of Korea
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35
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Zahoor F, Hussin FA, Isyaku UB, Gupta S, Khanday FA, Chattopadhyay A, Abbas H. Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing. DISCOVER NANO 2023; 18:36. [PMID: 37382679 PMCID: PMC10409712 DOI: 10.1186/s11671-023-03775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/17/2023] [Indexed: 06/30/2023]
Abstract
The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.
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Affiliation(s)
- Furqan Zahoor
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Fawnizu Azmadi Hussin
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Usman Bature Isyaku
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Shagun Gupta
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Farooq Ahmad Khanday
- Department of Electronics & Instrumentation Technology, University of Kashmir, Srinagar, India
| | - Anupam Chattopadhyay
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Haider Abbas
- Division of Material Science and Engineering, Hanyang University, Seoul, South Korea
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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36
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Cai H, Ao Z, Tian C, Wu Z, Liu H, Tchieu J, Gu M, Mackie K, Guo F. Brain Organoid Computing for Artificial Intelligence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530502. [PMID: 36909615 PMCID: PMC10002682 DOI: 10.1101/2023.02.28.530502] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware , living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware.
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37
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Jin P, Wang G, Chen L. Biphasic action potential and chaos in a symmetrical Chua Corsage Memristor-based circuit. CHAOS (WOODBURY, N.Y.) 2023; 33:023120. [PMID: 36859197 DOI: 10.1063/5.0138363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Neuromorphic computing provides unique computing and memory capabilities that could break the limitation of conventional von Neumann computing. Toward realizing neuromorphic computing, fabrication and synthetization of hardware elements and circuits to emulate biological neurons are crucial. Despite the striking progress in exploring neuron circuits, the existing circuits can only reproduce monophasic action potentials, and no studies report on circuits that could emulate biphasic action potentials, limiting the development of neuromorphic devices. Here, we present a simple third-order memristive circuit built with a classical symmetrical Chua Corsage Memristor (SCCM) to accurately emulate biological neurons and show that the circuit can reproduce monophasic action potentials, biphasic action potentials, and chaos. Applying the edge of chaos criterion, we calculate that the SCCM and the proposed circuit have the symmetrical edge of chaos domains with respect to the origin, which plays an important role in generating biphasic action potentials. Also, we draw a parameter classification map of the proposed circuit, showing the edge of chaos domain (EOCD), the locally active domain, and the locally passive domain. Near the calculated EOCD, the third-order circuit generates monophasic action potentials, biphasic action potentials, chaos, and ten types of symmetrical bi-directional neuromorphic phenomena by only tuning the input voltage, showing a resemblance to biological neurons. Finally, a physical SCCM circuit and some experimentally measured neuromorphic waveforms are exhibited. The experimental results agree with the numerical simulations, verifying that the proposed circuit is suitable as artificial neurons.
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Affiliation(s)
- Peipei Jin
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyi Wang
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Long Chen
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
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38
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Li Z, Tang W, Zhang B, Yang R, Miao X. Emerging memristive neurons for neuromorphic computing and sensing. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2188878. [PMID: 37090846 PMCID: PMC10120469 DOI: 10.1080/14686996.2023.2188878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Wei Tang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Rui Yang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
- CONTACT Rui Yang School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan430074, China; Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
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39
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Kagan BJ, Kitchen AC, Tran NT, Habibollahi F, Khajehnejad M, Parker BJ, Bhat A, Rollo B, Razi A, Friston KJ. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 2022; 110:3952-3969.e8. [PMID: 36228614 DOI: 10.1016/j.neuron.2022.09.001] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/21/2022] [Accepted: 08/31/2022] [Indexed: 11/06/2022]
Abstract
Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence.
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Affiliation(s)
| | | | - Nhi T Tran
- The Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Forough Habibollahi
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
| | - Moein Khajehnejad
- Department of Data Science and AI, Monash University, Melbourne, Australia
| | - Bradyn J Parker
- Department of Materials Science and Engineering, Monash University, Melbourne, VIC, Australia
| | - Anjali Bhat
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Ben Rollo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Adeel Razi
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK; Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia; Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Canada
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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40
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Wang T, Meng J, Zhou X, Liu Y, He Z, Han Q, Li Q, Yu J, Li Z, Liu Y, Zhu H, Sun Q, Zhang DW, Chen P, Peng H, Chen L. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat Commun 2022; 13:7432. [PMID: 36460675 PMCID: PMC9718838 DOI: 10.1038/s41467-022-35160-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.
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Affiliation(s)
- Tianyu Wang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jialin Meng
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Xufeng Zhou
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Yue Liu
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Zhenyu He
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qi Han
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingxuan Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jiajie Yu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Zhenhai Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Yongkai Liu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Hao Zhu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingqing Sun
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - David Wei Zhang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Peining Chen
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Huisheng Peng
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Lin Chen
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
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Yan X, Qian JH, Sangwan VK, Hersam MC. Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2108025. [PMID: 34813677 DOI: 10.1002/adma.202108025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/07/2021] [Indexed: 06/13/2023]
Abstract
Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.
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Affiliation(s)
- Xiaodong Yan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Justin H Qian
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
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42
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Zhou G, Ji X, Li J, Zhou F, Dong Z, Yan B, Sun B, Wang W, Hu X, Song Q, Wang L, Duan S. Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory. iScience 2022; 25:105240. [PMID: 36262310 PMCID: PMC9574501 DOI: 10.1016/j.isci.2022.105240] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/29/2022] [Accepted: 09/27/2022] [Indexed: 12/04/2022] Open
Abstract
Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.
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Affiliation(s)
- Guangdong Zhou
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Xiaoyue Ji
- College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China
| | - Jie Li
- Shenzhen-Hong Kong College of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Feichi Zhou
- Shenzhen-Hong Kong College of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhekang Dong
- College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China
| | - Bingtao Yan
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Bai Sun
- Department of Mechanics and Mechatronics Engineering, Centre for Advanced Materials Joining, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Wenhua Wang
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Xiaofang Hu
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Qunliang Song
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Lidan Wang
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Shukai Duan
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
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43
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Ying J, Liang Y, Wang G, Jin P, Chen L, Chen G. Action potential and chaos near the edge of chaos in memristive circuits. CHAOS (WOODBURY, N.Y.) 2022; 32:093101. [PMID: 36182357 DOI: 10.1063/5.0097075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Memristor-based neuromorphic systems have a neuro-bionic function, which is critical for possibly overcoming Moore's law limitation and the von Neumann bottleneck problem. To explore neural behaviors and complexity mechanisms in memristive circuits, this paper proposes an N-type locally active memristor, based on which a third-order memristive circuit is constructed. Theoretical analysis shows that the memristive circuit can exhibit not only various action potentials but also self-sustained oscillation and chaos. Based on Chua's theory of local activity, this paper finds that the neural behaviors and chaos emerge near the edge of chaos through subcritical Hopf bifurcation, in which the small unstable limit cycle is depicted by the dividing line between the attraction basin of the large stable limit cycle and the attraction basin of the stable equilibrium point. Furthermore, an analog circuit is designed to imitate the action potentials and chaos, and the simulation results are in agreement with the theoretical analysis.
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Affiliation(s)
- Jiajie Ying
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yan Liang
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guangyi Wang
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Peipei Jin
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Long Chen
- Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guanrong Chen
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
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44
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Xie B, Xiong T, Li W, Gao T, Zong J, Liu Y, Yu P. Perspective on Nanofluidic Memristors: from Mechanism to Application. Chem Asian J 2022; 17:e202200682. [PMID: 35994236 DOI: 10.1002/asia.202200682] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.
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Affiliation(s)
- Boyang Xie
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street, Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Tianyi Xiong
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street, Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Weiqi Li
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Tienan Gao
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Jianwei Zong
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, 100190, Beijing, CHINA
| | - Ying Liu
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, CHINA
| | - Ping Yu
- Chinese Academy of Sciences, Institute of Chemistry, North first street No. 2, zhonguancun, 100190, Beijing, CHINA
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45
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Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
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Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- *Correspondence: Valeri A. Makarov,
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
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46
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Wang X, Li H. A complementary resistive switching neuron. NANOTECHNOLOGY 2022; 33:355201. [PMID: 35605579 DOI: 10.1088/1361-6528/ac7241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
The complementary resistive switching (CRS) memristor has originally been proposed for use as the storage element or artificial synapse in large-scale crossbar array with the capability of solving the sneak path problem, but its usage has mainly been hampered by the inherent destructiveness of the read operation (switching '1' state to 'ON' or '0' state). Taking a different perspective on this 'undesired' property, we here report on the inherent behavioral similarity between the CRS memristor and a leaky integrate-and-fire (LIF) neuron which is another basic neural computing element, in addition to synapse. In particular, the mechanism behind the undesired read destructiveness for storage element and artificial synapse can be exploited to naturally realize the LIF and the ensuing spontaneous repolarization processes, followed by a refractory period. By means of this biological similarity, we demonstrate a Pt/Ta2O5-x/TaOy/Ta CRS memristor that can exhibit these neuronal behaviors and perform various fundamental neuronal operations, including additive/subtractive operations and coincidence detection. These results suggest that the CRS neuron, with its bio-interpretability, is a useful addition to the family of memristive neurons.
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Affiliation(s)
- Xinxin Wang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Huanglong Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, People's Republic of China
- Chinese Institute for Brain Research, Beijing 102206, People's Republic of China
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47
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Park SO, Jeong H, Park J, Bae J, Choi S. Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing. Nat Commun 2022; 13:2888. [PMID: 35660724 PMCID: PMC9166790 DOI: 10.1038/s41467-022-30539-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 05/04/2022] [Indexed: 01/08/2023] Open
Abstract
Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices. Designing energy efficient, uniform and reliable memristive devices for neuromorphic computing remains a challenge. By leveraging the self-rectifying behavior of gradual oxygen concentration of titanium dioxide, Choi et al. develop a transistor-free 1R cross-bar array with good uniformity and high yield.
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Affiliation(s)
- See-On Park
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hakcheon Jeong
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jongyong Park
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jongmin Bae
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Shinhyun Choi
- The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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48
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Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse. Nat Commun 2022; 13:2811. [PMID: 35589710 PMCID: PMC9120471 DOI: 10.1038/s41467-022-30432-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 04/25/2022] [Indexed: 12/02/2022] Open
Abstract
Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of synaptic plasticity has shown promising results after the advent of memristors. However, neuronal intrinsic plasticity, which involves in learning process through interactions with synaptic plasticity, has been rarely demonstrated. Synaptic and intrinsic plasticity occur concomitantly in learning process, suggesting the need of the simultaneous implementation. Here, we report a neurosynaptic device that mimics synaptic and intrinsic plasticity concomitantly in a single cell. Threshold switch and phase change memory are merged in threshold switch-phase change memory device. Neuronal intrinsic plasticity is demonstrated based on bottom threshold switch layer, which resembles the modulation of firing frequency in biological neuron. Synaptic plasticity is also introduced through the nonvolatile switching of top phase change layer. Intrinsic and synaptic plasticity are simultaneously emulated in a single cell to establish the positive feedback between them. A positive feedback learning loop which mimics the retraining process in biological system is implemented in threshold switch-phase change memory array for accelerated training. Synaptic plasticity and neuronal intrinsic plasticity are both involved in the learning process of hardware artificial neural network. Here, Lee et al. integrate a threshold switch and a phase change memory in a single device, which emulates biological synaptic and intrinsic plasticity simultaneously.
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49
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Nandi SK, Das SK, Cui Y, El Helou A, Nath SK, Ratcliff T, Raad P, Elliman RG. Thermal Conductivity of Amorphous NbO x Thin Films and Its Effect on Volatile Memristive Switching. ACS APPLIED MATERIALS & INTERFACES 2022; 14:21270-21277. [PMID: 35485924 DOI: 10.1021/acsami.2c04618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Metal-oxide-metal (MOM) devices based on niobium oxide exhibit threshold switching (or current-controlled negative differential resistance) due to thermally induced conductivity changes produced by Joule heating. A detailed understanding of the device characteristics therefore relies on an understanding of the thermal properties of the niobium oxide film and the MOM device structure. In this study, we use time-domain thermoreflectance to determine the thermal conductivity of amorphous NbOx films as a function of film composition and temperature. The thermal conductivity is shown to vary between 0.86 and 1.25 W·m-1·K-1 over the composition (x = 1.9 to 2.5) and temperature (293 to 453 K) ranges examined, and to increase with temperature for all compositions. The impact of these thermal conductivity variations on the quasistatic current-voltage (I-V) characteristics and oscillator dynamics of MOM devices is then investigated using a lumped-element circuit model. Understanding such effects is essential for engineering functional devices for nonvolatile memory and brain-inspired computing applications.
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Affiliation(s)
- Sanjoy Kumar Nandi
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
| | - Sujan Kumar Das
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
- Department of Physics, Jahangirnagar University, Dhaka 1342, Bangladesh
| | - Yubo Cui
- Mechanical Engineering Department, Southern Methodist University, Dallas, Texas 75275, United States
| | - Assaad El Helou
- Mechanical Engineering Department, Southern Methodist University, Dallas, Texas 75275, United States
| | - Shimul Kanti Nath
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, Western Australia 6009, Australia
| | - Thomas Ratcliff
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
| | - Peter Raad
- Mechanical Engineering Department, Southern Methodist University, Dallas, Texas 75275, United States
| | - Robert G Elliman
- Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra, Australian Capital Territory 2601, Australia
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50
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Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing. Nat Commun 2022; 13:2074. [PMID: 35440122 PMCID: PMC9018677 DOI: 10.1038/s41467-022-29727-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/28/2022] [Indexed: 12/18/2022] Open
Abstract
Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. “Reconfigurable memristors” that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes – volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices. Existing memristors cannot be reconfigured to meet the diverse switching requirements of various computing frameworks, limiting their universality. Here, the authors present a nanocrystal memristor that can be reconfigured on-demand to address these limitations
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