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Zhang S, Chen R, Kong D, Chen Y, Liu W, Jiang D, Zhao W, Chang C, Yang Y, Liu Y, Wei D. Photovoltaic nanocells for high-performance large-scale-integrated organic phototransistors. NATURE NANOTECHNOLOGY 2024; 19:1323-1332. [PMID: 38965348 DOI: 10.1038/s41565-024-01707-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/30/2024] [Indexed: 07/06/2024]
Abstract
A high-performance large-scale-integrated organic phototransistor needs a semiconductor layer that maintains its photoelectric conversion ability well during high-resolution pixelization. However, lacking a precise design for the nanoscale structure, a trade-off between photoelectric performance and device miniaturization greatly limits the success in commercial application. Here we demonstrate a photovoltaic-nanocell enhancement strategy, which overcomes the trade-off and enables high-performance organic phototransistors at a level beyond large-scale integration. Embedding a core-shell photovoltaic nanocell based on perovskite quantum dots in a photocrosslinkable organic semiconductor, ultralarge-scale-integrated (>221 units) imaging chips are manufactured using photolithography. 27 million pixels are interconnected and the pixel density is 3.1 × 106 units cm-2, at least two orders of magnitude higher than in existing organic imaging chips and equivalent to the latest commercial full-frame complementary metal-oxide-semiconductor camera chips. The embedded photovoltaic nanocells induce an in situ photogating modulation and enable photoresponsivity and detectivity of 6.8 × 106 A W-1 and 1.1 × 1013 Jones (at 1 Hz), respectively, achieving the highest values of organic imaging chips at large-scale or higher integration. In addition, a very-large-scale-integrated (>216 units) stretchable biomimetic retina based on photovoltaic nanocells is manufactured for neuromorphic imaging recognition with not only resolution but also photoresponsivity and power consumption approaching those of the biological counterpart.
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Affiliation(s)
- Shen Zhang
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Renzhong Chen
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Derong Kong
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Yiheng Chen
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Wentao Liu
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Dingding Jiang
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Weiyu Zhao
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Cheng Chang
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China
- Department of Macromolecular Science, Fudan University, Shanghai, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
| | - Yingguo Yang
- School of Microelectronics, Fudan University, Shanghai, China
| | - Yunqi Liu
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China
- Institute of Chemistry, Chinese Academy of Science, Beijing, China
| | - Dacheng Wei
- State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, China.
- Department of Macromolecular Science, Fudan University, Shanghai, China.
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, China.
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Tsai CH, Chen WC, Lin YC, Huang YH, Lin KW, Wu JY, Satoh T, Chen WC, Kuo CC. Ultralow-Energy-Consumption Photosynaptic Transistor Utilizing Conjugated Polymers/Perovskite Quantum Dots Nanocomposites With Ligand Density Optimization. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402567. [PMID: 39132749 DOI: 10.1002/smll.202402567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/18/2024] [Indexed: 08/13/2024]
Abstract
The photosynaptic transistor stands as a promising contender for overcoming the von Neumann bottleneck in the realm of photo-communication. In this context, photonic synaptic transistors is developed through a straightforward solution process, employing an organic semiconducting polymer with pendant-naphthalene-containing side chains (PDPPNA) in combination with ligand-density-engineered CsPbBr3 perovskite quantum dots (PQDs). This fabrication approach allows the devices to emulate fundamental synaptic behaviors, encompassing excitatory postsynaptic current, paired-pulse facilitation, the transition from short-to-long-term memory, and the concept of "learning experience." Notably, the phototransistor, incorporating the blend of the PDPPNA and CsPbBr3 PQDs washed with ethyl acetate, achieved an exceptional memory ratio of 104. Simultaneously, the same device exhibited an impressive paired-pulse facilitation ratio of 223% at a moderate operating voltage of -4 V and an extraordinarily low energy consumption of 0.215 aJ at an ultralow operating voltage of -0.1 mV. Consequently, these low-voltage synaptic devices, constructed with a pendant side-chain engineering of organic semiconductors and a ligand density engineering of PQDs through a simple fabrication process, exhibit substantial potential for replicating the visual memory capabilities of the human brain.
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Affiliation(s)
- Cheng-Hang Tsai
- Department of Molecular Science and Engineering, Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, 10608, Taiwan
| | - Wei-Cheng Chen
- Department of Chemical Engineering, National Taiwan University, Taipei, 10617, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei, 10617, Taiwan
| | - Yan-Cheng Lin
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei, 10617, Taiwan
- Department of Chemical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Yu-Hang Huang
- Department of Molecular Science and Engineering, Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, 10608, Taiwan
| | - Kai-Wei Lin
- Department of Molecular Science and Engineering, Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, 10608, Taiwan
| | - Jing-Yang Wu
- Department of Molecular Science and Engineering, Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, 10608, Taiwan
| | - Toshifumi Satoh
- Faculty of Engineering, Hokkaido University, Sapporo, 060-8628, Japan
- List Sustainable Digital Transformation Catalyst Collaboration Research Platform (ICReDD List-PF), Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo, 001-0021, Japan
| | - Wen-Chang Chen
- Department of Chemical Engineering, National Taiwan University, Taipei, 10617, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei, 10617, Taiwan
| | - Chi-Ching Kuo
- Department of Molecular Science and Engineering, Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, 10608, Taiwan
- Advanced Research Center for Green Materials Science and Technology, National Taiwan University, Taipei, 10617, Taiwan
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Zhao X, Zou H, Wang M, Wang J, Wang T, Wang L, Chen X. Conformal Neuromorphic Bioelectronics for Sense Digitalization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2403444. [PMID: 38934554 DOI: 10.1002/adma.202403444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/03/2024] [Indexed: 06/28/2024]
Abstract
Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.
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Affiliation(s)
- Xiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Haochen Zou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai, 200433, China
| | - Jianwu Wang
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lianhui Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaodong Chen
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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Zhu L, Lin J, Zhu Y, Wu J, Wan X, Sun H, Yu Z, Xu Y, Tan C. Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1195. [PMID: 39057872 PMCID: PMC11279808 DOI: 10.3390/nano14141195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/04/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024]
Abstract
Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.
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Affiliation(s)
- Li Zhu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Junchen Lin
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Yixin Zhu
- Yongjiang Laboratory (Y-LAB), Ningbo 315202, China;
| | - Jie Wu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Xiang Wan
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Huabin Sun
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Zhihao Yu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Yong Xu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Cheeleong Tan
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
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5
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Pashin DS, Bastrakova MV, Rybin DA, Soloviev II, Klenov NV, Schegolev AE. Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:854. [PMID: 38786810 PMCID: PMC11124324 DOI: 10.3390/nano14100854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/01/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.
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Affiliation(s)
- Dmitrii S. Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Marina V. Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
- Russian Quantum Centre, 143025 Moscow, Russia
| | - Dmitrii A. Rybin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Igor. I. Soloviev
- Russian Quantum Centre, 143025 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
| | - Nikolay V. Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey E. Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Science Department, Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
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6
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Chen B, Xue H, Pan H, Zhu L, Yan X, Wang J, Song Y, An Z. Reconfigurable memlogic long wave infrared sensing with superconductors. LIGHT, SCIENCE & APPLICATIONS 2024; 13:97. [PMID: 38670946 PMCID: PMC11053096 DOI: 10.1038/s41377-024-01424-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Optical sensors with in-cell logic and memory capabilities offer new horizons in realizing machine vision beyond von Neumann architectures and have been attempted with two-dimensional materials, memristive oxides, phase-changing materials etc. Noting the unparalleled performance of superconductors with both quantum-limited optical sensitivities and ultra-wide spectrum coverage, here we report a superconducting memlogic long-wave infrared sensor based on the bistability in hysteretic superconductor-normal phase transition. Driven cooperatively by electrical and optical pulses, the device offers deterministic in-sensor switching between resistive and superconducting (hence dissipationless) states with persistence > 105 s. This results in a resilient reconfigurable memlogic system applicable for, e.g., encrypted communications. Besides, a high infrared sensitivity at 12.2 μm is achieved through its in-situ metamaterial perfect absorber design. Our work opens the avenue to realize all-in-one superconducting memlogic sensors, surpassing biological retina capabilities in both sensitivity and wavelength, and presents a groundbreaking opportunity to integrate visional perception capabilities into superconductor-based intelligent quantum machines.
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Affiliation(s)
- Bingxin Chen
- State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing, Department of Physics, Fudan University, Shanghai, 200438, China
| | - Huanyi Xue
- State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing, Department of Physics, Fudan University, Shanghai, 200438, China
| | - Hong Pan
- State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing, Department of Physics, Fudan University, Shanghai, 200438, China
| | - Liping Zhu
- State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing, Department of Physics, Fudan University, Shanghai, 200438, China
| | - Xiaomi Yan
- ShanghaiTech Quantum Device Lab, ShanghaiTech University, Shanghai, 201210, China
| | - Jingzhe Wang
- ShanghaiTech Quantum Device Lab, ShanghaiTech University, Shanghai, 201210, China
| | - Yanru Song
- ShanghaiTech Quantum Device Lab, ShanghaiTech University, Shanghai, 201210, China.
| | - Zhenghua An
- State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing, Department of Physics, Fudan University, Shanghai, 200438, China.
- Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No. 701 Yunjin Road, Xuhui District, Shanghai, 200232, China.
- Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, 322000, Zhejiang, China.
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China.
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Karimov T, Ostrovskii V, Rybin V, Druzhina O, Kolev G, Butusov D. Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions. SENSORS (BASEL, SWITZERLAND) 2024; 24:2367. [PMID: 38610577 PMCID: PMC11014145 DOI: 10.3390/s24072367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/05/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ's applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system.
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Affiliation(s)
- Timur Karimov
- Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia; (T.K.); (V.O.)
| | - Valerii Ostrovskii
- Youth Research Institute, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia; (T.K.); (V.O.)
| | - Vyacheslav Rybin
- Computer-Aided Design Department, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova St., 197022 Saint Petersburg, Russia; (V.R.); (O.D.); (G.K.)
| | - Olga Druzhina
- Computer-Aided Design Department, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova St., 197022 Saint Petersburg, Russia; (V.R.); (O.D.); (G.K.)
| | - Georgii Kolev
- Computer-Aided Design Department, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova St., 197022 Saint Petersburg, Russia; (V.R.); (O.D.); (G.K.)
| | - Denis Butusov
- Computer-Aided Design Department, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova St., 197022 Saint Petersburg, Russia; (V.R.); (O.D.); (G.K.)
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8
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Neilo A, Bakurskiy S, Klenov N, Soloviev I, Kupriyanov M. Spin-Valve-Controlled Triggering of Superconductivity. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:245. [PMID: 38334516 PMCID: PMC11154576 DOI: 10.3390/nano14030245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/13/2024] [Accepted: 01/14/2024] [Indexed: 02/10/2024]
Abstract
We have studied the proximity effect in an SF1S1F2s superconducting spin valve consisting of a massive superconducting electrode (S) and a multilayer structure formed by thin ferromagnetic (F1,2) and superconducting (S1, s) layers. Within the framework of the Usadel equations, we have shown that changing the mutual orientation of the magnetization vectors of the F1,2 layers from parallel to antiparallel serves to trigger superconductivity in the outer thin s-film. We studied the changes in the pair potential in the outer s-film and found the regions of parameters with a significant spin-valve effect. The strongest effect occurs in the region of parameters where the pair-potential sign is changed in the parallel state. This feature reveals new ways to design devices with highly tunable inductance and critical current.
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Affiliation(s)
- Alexey Neilo
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia; (A.N.); (S.B.); (M.K.)
- National University of Science and Technology MISIS, Moscow 119049, Russia;
| | - Sergey Bakurskiy
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia; (A.N.); (S.B.); (M.K.)
- National University of Science and Technology MISIS, Moscow 119049, Russia;
| | - Nikolay Klenov
- National University of Science and Technology MISIS, Moscow 119049, Russia;
- Dukhov All-Russia Research Institute of Automatics, Moscow 101000, Russia
- Faculty of Physics, Moscow State University, Moscow 119991, Russia
| | - Igor Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia; (A.N.); (S.B.); (M.K.)
- National University of Science and Technology MISIS, Moscow 119049, Russia;
- Dukhov All-Russia Research Institute of Automatics, Moscow 101000, Russia
| | - Mikhail Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia; (A.N.); (S.B.); (M.K.)
- National University of Science and Technology MISIS, Moscow 119049, Russia;
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9
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Guo Z, Zhang J, Yang B, Li L, Liu X, Xu Y, Wu Y, Guo P, Sun T, Dai S, Liang H, Wang J, Zou Y, Xiong L, Huang J. Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2310155. [PMID: 38100140 DOI: 10.1002/adma.202310155] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.
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Affiliation(s)
- Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ben Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Shilei Dai
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Haixia Liang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jun Wang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yidong Zou
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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10
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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11
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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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12
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Pashin DS, Pikunov PV, Bastrakova MV, Schegolev AE, Klenov NV, Soloviev II. A bifunctional superconducting cell as flux qubit and neuron. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2023; 14:1116-1126. [PMID: 38034474 PMCID: PMC10682513 DOI: 10.3762/bjnano.14.92] [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: 09/01/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023]
Abstract
Josephson digital or analog ancillary circuits are an essential part of a large number of modern quantum processors. The natural candidate for the basis of tuning, coupling, and neromorphic co-processing elements for processors based on flux qubits is the adiabatic (reversible) superconducting logic cell. Using the simplest implementation of such a cell as an example, we have investigated the conditions under which it can optionally operate as an auxiliary qubit while maintaining its "classical" neural functionality. The performance and temperature regime estimates obtained confirm the possibility of practical use of a single-contact inductively shunted interferometer in a quantum mode in adjustment circuits for q-processors.
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Affiliation(s)
- Dmitrii S Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Pavel V Pikunov
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Marina V Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
- Russian Quantum Center, 143025 Skolkovo, Moscow, Russia
| | - Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- National University of Science and Technology MISIS, 119049 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- National University of Science and Technology MISIS, 119049 Moscow, Russia
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13
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Yang C, Su L, Xia K, Li X, Liu Y, Li H. Doping-modulated lateral asymmetric Schottky diode as a high-performance self-powered synaptic device. OPTICS EXPRESS 2023; 31:31061-31071. [PMID: 37710634 DOI: 10.1364/oe.498708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023]
Abstract
In the post-Moore era, the gradually saturated computational capability of conventional digital computers showing the opposite trend as the exponentially increasing data volumes imperatively required a platform or technology to break this bottleneck. Brain-inspired neuromorphic computing promises to inherently improve the efficiency of information processing and computation by means of the highly parallel hardware architecture to reduce global data transmission. Here, we demonstrate a compact device technology based on the barrier asymmetry to achieve zero-consumption self-powered synaptic devices. In order to tune the device behaviors, the typical chemical doping is used to tailor the asymmetry for energy harvesting. Finally, in our demonstrated devices, the open-circuit voltage (VOC) and power-conversion efficiency (PCE) can be modulated up to 0.77 V and 6%, respectively. Optimized photovoltaic features affords synaptic devices with an outstanding programming weight states, involving training facilitation, stimulus reinforce and consolidation. Based on self-powered system, this work further presents a highly available modulation scheme, which achieves excellent device behaviors while ensuring the zero-energy consumption.
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14
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Schegolev AE, Klenov NV, Gubochkin GI, Kupriyanov MY, Soloviev II. Bio-Inspired Design of Superconducting Spiking Neuron and Synapse. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2101. [PMID: 37513112 PMCID: PMC10383304 DOI: 10.3390/nano13142101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.
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Affiliation(s)
- Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
| | - Georgy I Gubochkin
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Russian Quantum Center, 100 Novaya Street, Skolkovo, 143025 Moscow, Russia
| | - Mikhail Yu Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
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15
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Neilo A, Bakurskiy S, Klenov N, Soloviev I, Kupriyanov M. Tunnel Josephson Junction with Spin-Orbit/Ferromagnetic Valve. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:1970. [PMID: 37446484 DOI: 10.3390/nano13131970] [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: 05/30/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023]
Abstract
We have theoretically studied the transport properties of the SIsNSOF structure consisting of thick (S) and thin (s) films of superconductor, an insulator layer (I), a thin film of normal metal with spin-orbit interaction (SOI) (NSO), and a monodomain ferromagnetic layer (F). The interplay between superconductivity, ferromagnetism, and spin-orbit interaction allows the critical current of this Josephson junction to be smoothly varied over a wide range by rotating the magnetization direction in the single F-layer. We have studied the amplitude of the spin valve effect and found the optimal ranges of parameters.
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Affiliation(s)
- Alexey Neilo
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Sergey Bakurskiy
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
| | - Igor Soloviev
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Mikhail Kupriyanov
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
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16
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El Hage R, Humbert V, Rouco V, Sánchez-Santolino G, Lagarrigue A, Seurre K, Carreira SJ, Sander A, Charliac J, Mesoraca S, Trastoy J, Briatico J, Santamaría J, Villegas JE. Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction. Nat Commun 2023; 14:3010. [PMID: 37230971 DOI: 10.1038/s41467-023-38608-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.
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Affiliation(s)
- Ralph El Hage
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Vincent Humbert
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Victor Rouco
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Gabriel Sánchez-Santolino
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Aurelien Lagarrigue
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Kevin Seurre
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Santiago J Carreira
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Anke Sander
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jérôme Charliac
- Laboratoire de Physique des Interfaces et des Couches Minces (UMR7647), CNRS, Ecole Polytechnique, 91128, Palaiseau Cedex, France
| | - Salvatore Mesoraca
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Javier Briatico
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jacobo Santamaría
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Javier E Villegas
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
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17
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Iankevich G, Sarkar A, Katnagallu S, Chellali MR, Wang D, Velasco L, Singh R, Reisinger T, Kruk R, Hahn H. A New Class of Cluster-Matrix Nanocomposite Made of Fully Miscible Components. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208774. [PMID: 36434806 DOI: 10.1002/adma.202208774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/09/2022] [Indexed: 06/16/2023]
Abstract
Nanocomposite materials, consisting of two or more phases, at least one of which has a nanoscale dimension, play a distinctive role in materials science because of the multiple possibilities for tailoring their structural properties and, consequently, their functionalities. In addition to the challenges of controlling the size, size distribution, and volume fraction of nanometer phases, thermodynamic stability conditions limit the choice of constituent materials. This study goes beyond this limitation by showing the possibility of achieving nanocomposites from a bimetallic system, which exhibits complete miscibility under equilibrium conditions. A series of nanocomposite samples with different compositions are synthesized by the co-deposition of 2000-atom Ni-clusters and a flux of Cu-atoms using a novel cluster ion beam deposition system. The retention of the metastable nanostructure is ascertained from atom probe tomography (APT), magnetometry, and magnetotransport studies. APT confirms the presence of nanoscale regions with ≈100 at% Ni. Magnetometry and magnetotransport studies reveal superparamagnetic behavior and magnetoresistance stemming from the single-domain ferromagnetic Ni-clusters embedded in the Cu-matrix. Essentially, the magnetic properties of the nanocomposites can be tailored by the precise control of the Ni concentration. The initial results offer a promising direction for future research on nanocomposites consisting of fully miscible elements.
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Affiliation(s)
- Gleb Iankevich
- Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Abhishek Sarkar
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- KIT-TUD Joint Laboratory Nanomaterials, Technical University Darmstadt, 64287, Darmstadt, Germany
| | - Shyam Katnagallu
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Department of Computational Materials Design, Max Planck Institut fÜr Eisenforschung GmbH, Max-Planck- Str. 1, 40237, Düsseldorf, Germany
| | - Mohammed Reda Chellali
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Di Wang
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Leonardo Velasco
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Direccion Academica, Universidad Nacional de Colombia Sede de La Paz, Km 9 via Valledupar-La Paz, Cesar, 202017, Colombia
| | - Ruby Singh
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Thomas Reisinger
- Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Robert Kruk
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Horst Hahn
- Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- KIT-TUD Joint Laboratory Nanomaterials, Technical University Darmstadt, 64287, Darmstadt, Germany
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18
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Hejda M, Malysheva E, Owen-Newns D, Ali Al-Taai QR, Zhang W, Ortega-Piwonka I, Javaloyes J, Wasige E, Dolores-Calzadilla V, Figueiredo JML, Romeira B, Hurtado A. Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser. NANOPHOTONICS 2023; 12:857-867. [PMID: 36909291 PMCID: PMC9995654 DOI: 10.1515/nanoph-2022-0362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/26/2022] [Indexed: 06/18/2023]
Abstract
Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.
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Affiliation(s)
- Matěj Hejda
- SUPA Department of Physics, Institute of Photonics, University of Strathclyde, Glasgow, UK
| | - Ekaterina Malysheva
- Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Dafydd Owen-Newns
- SUPA Department of Physics, Institute of Photonics, University of Strathclyde, Glasgow, UK
| | | | - Weikang Zhang
- SUPA Department of Physics, Institute of Photonics, University of Strathclyde, Glasgow, UK
| | | | - Julien Javaloyes
- Dept de Física and IAC-3, Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Edward Wasige
- High Frequency Electronics Group, University of Glasgow, Glasgow, UK
| | | | - José M. L. Figueiredo
- Centra-Ciências and Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Bruno Romeira
- INL – International Iberian Nanotechnology Laboratory, Ultrafast Bio- and Nanophotonics Group, Braga, Portugal
| | - Antonio Hurtado
- SUPA Department of Physics, Institute of Photonics, University of Strathclyde, Glasgow, UK
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19
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Zhang Q, Li E, Wang Y, Gao C, Wang C, Li L, Geng D, Chen H, Chen W, Hu W. Ultralow-Power Vertical Transistors for Multilevel Decoding Modes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208600. [PMID: 36341511 DOI: 10.1002/adma.202208600] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralow-power vertical transistor is demonstrated based on transition-metal carbides/nitrides (MXene) and organic single crystal. The transistor exhibits a high JON of 16.6 mA cm-2 and a high JON /JOFF ratio of 9.12 × 105 under an ultralow working voltage of -1 mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.
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Affiliation(s)
- Qing Zhang
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Enlong Li
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Yongshuai Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Changsong Gao
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Congyong Wang
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Lin Li
- Institute of Molecular Plus, Tianjin University, Tianjin, 300072, China
| | - Dechao Geng
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
| | - Huipeng Chen
- National and Local United Engineering Lab of Flat Panel Display Technology, Institute of Optoelectronic Display, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Wei Chen
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Wenping Hu
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China
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20
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Neilo A, Bakurskiy S, Klenov N, Soloviev I, Kupriyanov M. Superconducting Valve Exploiting Interplay between Spin-Orbit and Exchange Interactions. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:4426. [PMID: 36558279 PMCID: PMC9785097 DOI: 10.3390/nano12244426] [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: 11/21/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
We theoretically investigated the proximity effect in SNSOF and SF'F structures consisting of a superconductor (S), a normal metal (NSO), and ferromagnetic (F',F) thin films with spin-orbit interaction (SOI) in the NSO layer. We show that a normal layer with spin-orbit interaction effectively suppresses triplet correlations generated in a ferromagnetic layer. Due to this effect, the critical temperature of the superconducting layer in the SNSOF multilayer turns out to be higher than in a similar multilayer without spin-orbit interaction in the N layer. Moreover, in the presence of a mixed type of spin-orbit interaction involving the Rashba and Dresselhaus components, the SNSOF structure is a spin valve, whose critical temperature is determined by the direction of the magnetization vector in the F layer. We calculated the control characteristics of the SNSOF spin valve and compared them with those available in traditional SF'F devices with two ferromagnetic layers. We concluded that SNSOF structures with one controlled F layer provide solid advantages over the broadly considered SF'F spin valves, paving the way for high-performance storage components for superconducting electronics.
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Affiliation(s)
- Alexey Neilo
- National University of Science and Technology MISIS, 119049 Moscow, Russia
| | - Sergey Bakurskiy
- National University of Science and Technology MISIS, 119049 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia
| | - Igor Soloviev
- National University of Science and Technology MISIS, 119049 Moscow, Russia
| | - Mikhail Kupriyanov
- National University of Science and Technology MISIS, 119049 Moscow, Russia
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21
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Yang Q, Mishra R, Cen Y, Shi G, Sharma R, Fong X, Yang H. Spintronic Integrate-Fire-Reset Neuron with Stochasticity for Neuromorphic Computing. NANO LETTERS 2022; 22:8437-8444. [PMID: 36260522 DOI: 10.1021/acs.nanolett.2c02409] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Spintronics has been recently extended to neuromorphic computing because of its energy efficiency and scalability. However, a biorealistic spintronic neuron with probabilistic "spiking" and a spontaneous reset functionality has not been demonstrated yet. Here, we propose a biorealistic spintronic neuron device based on the heavy metal (HM)/ferromagnet (FM)/antiferromagnet (AFM) spin-orbit torque (SOT) heterostructure. The spintronic neuron can autoreset itself after firing due to the exchange bias of the AFM. The firing process is inherently stochastic because of the competition between the SOT and AFM pinning effects. We also implement a restricted Boltzmann machine (RBM) and stochastic integration multilayer perceptron (SI-MLP) using our proposed neuron. Despite the bit-width limitation, the proposed spintronic model can achieve an accuracy of 97.38% in pattern recognition, which is even higher than the baseline accuracy (96.47%). Our results offer a spintronic device solution to emulate biologically realistic spiking neurons.
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Affiliation(s)
- Qu Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Rahul Mishra
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
- Centre for Applied Research in Electronics, Indian Institute of Technology Delhi, New Delhi, India 110016
| | - Yunuo Cen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Guoyi Shi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Raghav Sharma
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Xuanyao Fong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Hyunsoo Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
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22
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Petrov AV, Nikitin SI, Tagirov LR, Gumarov AI, Yanilkin IV, Yusupov RV. Ultrafast signatures of magnetic inhomogeneity in Pd 1- x Fe x ( x ≤ 0.08) epitaxial thin films. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2022; 13:836-844. [PMID: 36105688 PMCID: PMC9443348 DOI: 10.3762/bjnano.13.74] [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: 03/31/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
A series of Pd1- x Fe x alloy epitaxial films (x = 0, 0.038, 0.062, and 0.080), a material promising for superconducting spintronics, was prepared and studied with ultrafast optical and magneto-optical laser spectroscopy in a wide temperature range of 4-300 K. It was found that the transition to the ferromagnetic state causes a qualitative change of both the reflectivity and the magneto-optical Kerr effect transients. A nanoscale magnetic inhomogeneity of the ferromagnet/paramagnet type inherent in the palladium-rich Pd1- x Fe x alloys reveals itself through the occurrence of a relatively slow, 10-25 ps, photoinduced demagnetization component following a subpicosecond one; the former vanishes at low temperatures only in the x = 0.080 sample. We argue that the 10 ps timescale demagnetization originates most probably from the diffusive transport of d electrons under the condition of nanoscale magnetic inhomogeneities. The low-temperature fraction of the residual paramagnetic phase can be deduced from the magnitude of the slow reflectivity relaxation component. It is estimated as ≈30% for x = 0.038 and ≈15% for x = 0.062 films. The minimal iron content ensuring the magnetic homogeneity of the ferromagnetic state in the Pd1- x Fe x alloy at low temperatures is about 7-8 atom %.
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Affiliation(s)
| | | | - Lenar R Tagirov
- Zavoisky Physical-Technical Institute, FRC Kazan Scientific Centre of RAS, Sibirsky trakt 10/7, Kazan, Russia
| | - Amir I Gumarov
- Kazan Federal University, Kremlyovskaya 18, Kazan, Russia
- Zavoisky Physical-Technical Institute, FRC Kazan Scientific Centre of RAS, Sibirsky trakt 10/7, Kazan, Russia
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23
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Bastrakova MV, Pashin DS, Rybin DA, Schegolev AE, Klenov NV, Soloviev II, Gorchavkina AA, Satanin AM. A superconducting adiabatic neuron in a quantum regime. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2022; 13:653-665. [PMID: 35923170 PMCID: PMC9296986 DOI: 10.3762/bjnano.13.57] [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: 04/20/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations. The obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function.
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Affiliation(s)
- Marina V Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
| | - Dmitrii S Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
| | - Dmitriy A Rybin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
| | - Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Igor I Soloviev
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
| | - Anastasiya A Gorchavkina
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603950 Nizhny Novgorod, Russia
- Higher School of Economics, Russia National Research University, 101000 Moscow, Russia
| | - Arkady M Satanin
- Higher School of Economics, Russia National Research University, 101000 Moscow, Russia
- Federal State Unitary Enterprise All-Russia Research Institute of Automatics named after N.L. Dukhov, 101000 Moscow, Russia
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24
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Schegolev AE, Klenov NV, Bakurskiy SV, Soloviev II, Kupriyanov MY, Tereshonok MV, Sidorenko AS. Tunable superconducting neurons for networks based on radial basis functions. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2022; 13:444-454. [PMID: 35655940 PMCID: PMC9127244 DOI: 10.3762/bjnano.13.37] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron. Special attention is paid to tuning the activation functions to a Gaussian form with relatively large amplitude. For the practical implementation of the required tunability, we proposed and investigated heterostructures designed for the implementation of adjustable inductors that consist of superconducting, ferromagnetic, and normal layers.
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Affiliation(s)
- Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Lobachevsky State University of Nizhni Novgorod Faculty of Physics, 603950 Nizhny Novgorod, Russia
| | - Sergey V Bakurskiy
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Dukhov All-Russia Research Institute of Automatics, 101000 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Mikhail Yu Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Maxim V Tereshonok
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Anatoli S Sidorenko
- Institute of Electronic Engineering and Nanotechnologies ASM, MD2028 Kishinev, Moldova
- Laboratory of Functional Nanostructures, Orel State University named after I.S. Turgenev, 302026, Orel, Russia
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25
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Superconducting Bio-Inspired Au-Nanowire-Based Neurons. NANOMATERIALS 2022; 12:nano12101671. [PMID: 35630895 PMCID: PMC9147065 DOI: 10.3390/nano12101671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. A comparison with the Izhikevich mathematical model of biological neurons is carried out.
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26
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Shi J, Jie J, Deng W, Luo G, Fang X, Xiao Y, Zhang Y, Zhang X, Zhang X. A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200380. [PMID: 35243701 DOI: 10.1002/adma.202200380] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.
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Affiliation(s)
- Jialin Shi
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jiansheng Jie
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
- Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR, 999078, P. R. China
| | - Wei Deng
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Gan Luo
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaochen Fang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yanling Xiao
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yujian Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiujuan Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaohong Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
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27
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Building block 3D printing based on molecular self-assembly monolayer with self-healing properties. Sci Rep 2022; 12:6806. [PMID: 35474113 PMCID: PMC9043216 DOI: 10.1038/s41598-022-10875-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/14/2022] [Indexed: 11/08/2022] Open
Abstract
The spontaneous formation of biological substances, such as human organs, are governed by different stimuli driven by complex 3D self-organization protocols at the molecular level. The fundamentals of such molecular self-assembly processes are critical for fabrication of advanced technological components in nature. We propose and experimentally demonstrate a promising 3D printing method with self-healing property based on molecular self-assembly-monolayer principles, which is conceptually different than the existing 3D printing protocols. The proposed molecular building-block approach uses metal ion-mediated continuous self-assembly of organic molecular at liquid-liquid interfaces to create 2D and 3D structures. Using this technique, we directly printed nanosheets and 3D rods using dithiol molecules as building block units.
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28
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Yalcin G, Themeli E, Stamhuis E, Philipsen S, Puntoni S. Perceptions of Justice By Algorithms. ARTIFICIAL INTELLIGENCE AND LAW 2022; 31:269-292. [PMID: 37070085 PMCID: PMC10102053 DOI: 10.1007/s10506-022-09312-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 02/28/2022] [Accepted: 03/04/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they trust human judges more and have greater intentions to go to the court when a human (vs. an algorithmic) judge adjudicates. Additionally, we demonstrate that the extent that individuals trust algorithmic and human judges depends on the nature of the case: trust for algorithmic judges is especially low when legal cases involve emotional complexities (vs. technically complex or uncomplicated cases). SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10506-022-09312-z.
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Affiliation(s)
- Gizem Yalcin
- Rotterdam School of Management, Erasmus University Rotterdam, Postbus 1738, 3000 DR Rotterdam, Netherlands
| | - Erlis Themeli
- Erasmus School of Law, Erasmus University Rotterdam, Postbus 1738, 3000 DR, Rotterdam, Netherlands
| | - Evert Stamhuis
- Erasmus School of Law, Erasmus University Rotterdam, Postbus 1738, 3000 DR, Rotterdam, Netherlands
| | - Stefan Philipsen
- Montaigne Centre for Rule of Law and Administration of Justice, Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, Netherlands
| | - Stefano Puntoni
- Rotterdam School of Management, Erasmus University Rotterdam, Postbus 1738, 3000 DR Rotterdam, Netherlands
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29
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Yang W, Lin Y, Inagaki S, Shimizu H, Ercan E, Hsu L, Chueh C, Higashihara T, Chen W. Low-Energy-Consumption and Electret-Free Photosynaptic Transistor Utilizing Poly(3-hexylthiophene)-Based Conjugated Block Copolymers. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105190. [PMID: 35064648 PMCID: PMC8922097 DOI: 10.1002/advs.202105190] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/03/2022] [Indexed: 05/14/2023]
Abstract
Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning-forgetting-relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 105 , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of -0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology.
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Affiliation(s)
- Wei‐Chen Yang
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Yan‐Cheng Lin
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Shin Inagaki
- Department of Organic Materials ScienceGraduate School of Organic Materials ScienceYamagata UniversityYonezawaYamagata992‐8510Japan
| | - Hiroya Shimizu
- Department of Organic Materials ScienceGraduate School of Organic Materials ScienceYamagata UniversityYonezawaYamagata992‐8510Japan
| | - Ender Ercan
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Li‐Che Hsu
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
- Institute of Polymer Science and EngineeringNational Taiwan UniversityTaipei10617Taiwan
| | - Chu‐Chen Chueh
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
| | - Tomoya Higashihara
- Department of Organic Materials ScienceGraduate School of Organic Materials ScienceYamagata UniversityYonezawaYamagata992‐8510Japan
| | - Wen‐Chang Chen
- Department of Chemical EngineeringNational Taiwan UniversityTaipei10617Taiwan
- Advanced Research Center for Green Materials Science and TechnologyNational Taiwan UniversityTaipei10617Taiwan
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30
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Tschirhart P, Segall K. BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics. Front Neurosci 2021; 15:750748. [PMID: 34992515 PMCID: PMC8724521 DOI: 10.3389/fnins.2021.750748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Abstract
Superconducting electronics (SCE) is uniquely suited to implement neuromorphic systems. As a result, SCE has the potential to enable a new generation of neuromorphic architectures that can simultaneously provide scalability, programmability, biological fidelity, on-line learning support, efficiency and speed. Supporting all of these capabilities simultaneously has thus far proven to be difficult using existing semiconductor technologies. However, as the fields of computational neuroscience and artificial intelligence (AI) continue to advance, the need for architectures that can provide combinations of these capabilities will grow. In this paper, we will explain how superconducting electronics could be used to address this need by combining analog and digital SCE circuits to build large scale neuromorphic systems. In particular, we will show through detailed analysis that the available SCE technology is suitable for near term neuromorphic demonstrations. Furthermore, this analysis will establish that neuromorphic architectures built using SCE will have the potential to be significantly faster and more efficient than current approaches, all while supporting capabilities such as biologically suggestive neuron models and on-line learning. In the future, SCE-based neuromorphic systems could serve as experimental platforms supporting investigations that are not feasible with current approaches. Ultimately, these systems and the experiments that they support would enable the advancement of neuroscience and the development of more sophisticated AI.
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Affiliation(s)
- Paul Tschirhart
- Advanced Technology Laboratory, Northrop Grumman, Linthicum, MD, United States
| | - Ken Segall
- Advanced Technology Laboratory, Northrop Grumman, Linthicum, MD, United States
- Department of Physics and Astronomy, Colgate University, Hamilton, NY, United States
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31
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Sarkar K, Bonnerjee D, Srivastava R, Bagh S. A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing. Chem Sci 2021; 12:15821-15832. [PMID: 35024106 PMCID: PMC8672730 DOI: 10.1039/d1sc01505b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/08/2021] [Indexed: 11/21/2022] Open
Abstract
Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.
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Affiliation(s)
- Kathakali Sarkar
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Deepro Bonnerjee
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Rajkamal Srivastava
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
| | - Sangram Bagh
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute (HBNI) Block A/F, Sector-I, Bidhannagar Kolkata 700064 India
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32
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Cheng R, Goteti US, Walker H, Krause KM, Oeding L, Hamilton MC. Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions. Front Neurosci 2021; 15:765883. [PMID: 34819835 PMCID: PMC8606638 DOI: 10.3389/fnins.2021.765883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
We explore the use of superconducting quantum phase slip junctions (QPSJs), an electromagnetic dual to Josephson Junctions (JJs), in neuromorphic circuits. These small circuits could serve as the building blocks of neuromorphic circuits for machine learning applications because they exhibit desirable properties such as inherent ultra-low energy per operation, high speed, dense integration, negligible loss, and natural spiking responses. In addition, they have a relatively straight-forward micro/nano fabrication, which shows promise for implementation of an enormous number of lossless interconnections that are required to realize complex neuromorphic systems. We simulate QPSJ-only, as well as hybrid QPSJ + JJ circuits for application in neuromorphic circuits including artificial synapses and neurons, as well as fan-in and fan-out circuits. We also design and simulate learning circuits, where a simplified spike timing dependent plasticity rule is realized to provide potential learning mechanisms. We also take an alternative approach, which shows potential to overcome some of the expected challenges of QPSJ-based neuromorphic circuits, via QPSJ-based charge islands coupled together to generate non-linear charge dynamics that result in a large number of programmable weights or non-volatile memory states. Notably, we show that these weights are a function of the timing and frequency of the input spiking signals and can be programmed using a small number of DC voltage bias signals, therefore exhibiting spike-timing and rate dependent plasticity, which are mechanisms to realize learning in neuromorphic circuits.
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Affiliation(s)
- Ran Cheng
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
| | - Uday S Goteti
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Harrison Walker
- Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States.,Department of Materials Engineering, Auburn University, Auburn, AL, United States
| | - Keith M Krause
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
| | - Luke Oeding
- Department of Mathematics and Statistics, Auburn University, Auburn, AL, United States
| | - Michael C Hamilton
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.,Alabama Micro/Nano Science and Technology Center, Auburn University, Auburn, AL, United States
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33
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Bian H, Goh YY, Liu Y, Ling H, Xie L, Liu X. Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2006469. [PMID: 33837601 DOI: 10.1002/adma.202006469] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/03/2020] [Indexed: 06/12/2023]
Abstract
Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.
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Affiliation(s)
- Hongyu Bian
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Yi Yiing Goh
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 119077, Singapore
| | - Yuxia Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Center for Functional Materials, National University of Singapore Suzhou Research Institute, Suzhou, 215123, China
| | - Haifeng Ling
- Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Linghai Xie
- Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaogang Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Center for Functional Materials, National University of Singapore Suzhou Research Institute, Suzhou, 215123, China
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34
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Wang Z, Wang L, Wu Y, Bian L, Nagai M, Jv R, Xie L, Ling H, Li Q, Bian H, Yi M, Shi N, Liu X, Huang W. Signal Filtering Enabled by Spike Voltage-Dependent Plasticity in Metalloporphyrin-Based Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2104370. [PMID: 34510593 DOI: 10.1002/adma.202104370] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/25/2021] [Indexed: 06/13/2023]
Abstract
Neural systems can selectively filter and memorize spatiotemporal information, thus enabling high-efficient information processing. Emulating such an exquisite biological process in electronic devices is of fundamental importance for developing neuromorphic architectures with efficient in situ edge/parallel computing, and probabilistic inference. Here a novel multifunctional memristor is proposed and demonstrated based on metalloporphyrin/oxide hybrid heterojunction, in which the metalloporphyrin layer allows for dual electronic/ionic transport. Benefiting from the coordination-assisted ionic diffusion, the device exhibits smooth, gradual conductive transitions. It is shown that the memristive characteristics of this hybrid system can be modulated by altering the metal center for desired metal-oxygen bonding energy and oxygen ions migration dynamics. The spike voltage-dependent plasticity stemming from the local/extended movement of oxygen ions under low/high voltage is identified, which permits potentiation and depression under unipolar different positive voltages. As a proof-of-concept demonstration, memristive arrays are further built to emulate the signal filtering function of the biological visual system. This work demonstrates the ionic intelligence feature of metalloporphyrin and paves the way for implementing efficient neural-signal analysis in neuromorphic hardware.
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Affiliation(s)
- Zhiyong Wang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Laiyuan Wang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Yiming Wu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, Singapore, 138634, Singapore
| | - Linyi Bian
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Masaru Nagai
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China
| | - Ruolin Jv
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Linghai Xie
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Haifeng Ling
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Qi Li
- Physical Science Division, IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Hongyu Bian
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
| | - Mingdong Yi
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Naien Shi
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
| | - Xiaogang Liu
- Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, Singapore, 138634, Singapore
- Joint School of National University of Singapore and Tianjin, University International Campus of Tianjin University, Fuzhou, 350207, China
| | - Wei Huang
- Center for Molecular Systems & Organic Devices (CMSOD), Key Laboratory for Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, 210023, China
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
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35
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Dynamic Processes in a Superconducting Adiabatic Neuron with Non-Shunted Josephson Contacts. Symmetry (Basel) 2021. [DOI: 10.3390/sym13091735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
We investigated the dynamic processes in a superconducting neuron based on Josephson contacts without resistive shunting (SC-neuron). Such a cell is a key element of perceptron-type neural networks that operate in both classical and quantum modes. The analysis of the obtained results allowed us to find the mode when the transfer characteristic of the element implements the “sigmoid” activation function. The numerical approach to the analysis of the equations of motion and the Monte Carlo method revealed the influence of inertia (capacitances), dissipation, and temperature on the dynamic characteristics of the neuron.
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36
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Primavera BA, Shainline JM. Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware. Front Neurosci 2021; 15:732368. [PMID: 34552465 PMCID: PMC8450355 DOI: 10.3389/fnins.2021.732368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022] Open
Abstract
Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
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Affiliation(s)
- Bryce A. Primavera
- National Institute of Standards and Technology, Boulder, CO, United States
- Department of Physics, University of Colorado Boulder, Boulder, CO, United States
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37
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Goteti US, Zaluzhnyy IA, Ramanathan S, Dynes RC, Frano A. Low-temperature emergent neuromorphic networks with correlated oxide devices. Proc Natl Acad Sci U S A 2021; 118:e2103934118. [PMID: 34433669 PMCID: PMC8536335 DOI: 10.1073/pnas.2103934118] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Neuromorphic computing-which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, and dendrites-offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.
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Affiliation(s)
- Uday S Goteti
- Department of Physics, University of California San Diego, La Jolla, CA 92093
| | - Ivan A Zaluzhnyy
- Department of Physics, University of California San Diego, La Jolla, CA 92093
| | - Shriram Ramanathan
- School of Materials Engineering, Purdue University, West Lafayette, IN 47907
| | - Robert C Dynes
- Department of Physics, University of California San Diego, La Jolla, CA 92093;
| | - Alex Frano
- Department of Physics, University of California San Diego, La Jolla, CA 92093;
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38
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Theoretical Basis of Quantum-Mechanical Modeling of Functional Nanostructures. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The paper presents an analytical review of theoretical methods for modeling functional nanostructures. The main evolutionary changes in the approaches of quantum-mechanical modeling are described. The foundations of the first-principal theory are considered, including the stationery and time-dependent Schrödinger equations, wave functions, the form of writing energy operators, and the principles of solving equations. The idea and specifics of describing the motion and interaction of nuclei and electrons in the framework of the theory of the electron density functional are presented. Common approximations and approaches in the methods of quantum mechanics are presented, including the Born–Oppenheimer approximation, the Hartree–Fock approximation, the Thomas–Fermi theory, the Hohenberg–Kohn theorems, and the Kohn–Sham formalism. Various options for describing the exchange–correlation energy in the theory of the electron density functional are considered, such as the local density approximation, generalized and meta-generalized gradient approximations, and hybridization of the generalized gradient method. The development of methods of quantum mechanics to quantum molecular dynamics or the dynamics of Car–Parrinello is shown. The basic idea of combining classical molecular modeling with calculations of the electronic structure, which is reflected in the potentials of the embedded atom, is described.
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39
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Mishra A, Ghosh S, Kumar Dana S, Kapitaniak T, Hens C. Neuron-like spiking and bursting in Josephson junctions: A review. CHAOS (WOODBURY, N.Y.) 2021; 31:052101. [PMID: 34240928 DOI: 10.1063/5.0050526] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/30/2021] [Indexed: 06/13/2023]
Abstract
The superconducting Josephson junction shows spiking and bursting behaviors, which have similarities with neuronal spiking and bursting. This phenomenon had been observed long ago by some researchers; however, they overlooked the biological similarity of this particular dynamical feature and never attempted to interpret it from the perspective of neuronal dynamics. In recent times, the origin of such a strange property of the superconducting junction has been explained and such neuronal functional behavior has also been observed in superconducting nanowires. The history of this research is briefly reviewed here with illustrations from studies of two junction models and their dynamical interpretation in the sense of biological bursting.
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Affiliation(s)
- Arindam Mishra
- Department of Mathematics, Jadavpur University, Kolkata 700032, India
| | - Subrata Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
| | - Syamal Kumar Dana
- Department of Mathematics, Jadavpur University, Kolkata 700032, India
| | - Tomasz Kapitaniak
- Division of Dynamics, Lodz University of Technology, 90-924 Lodz, Poland
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
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40
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Berggren K, Xia Q, Likharev KK, Strukov DB, Jiang H, Mikolajick T, Querlioz D, Salinga M, Erickson JR, Pi S, Xiong F, Lin P, Li C, Chen Y, Xiong S, Hoskins BD, Daniels MW, Madhavan A, Liddle JA, McClelland JJ, Yang Y, Rupp J, Nonnenmann SS, Cheng KT, Gong N, Lastras-Montaño MA, Talin AA, Salleo A, Shastri BJ, de Lima TF, Prucnal P, Tait AN, Shen Y, Meng H, Roques-Carmes C, Cheng Z, Bhaskaran H, Jariwala D, Wang H, Shainline JM, Segall K, Yang JJ, Roy K, Datta S, Raychowdhury A. Roadmap on emerging hardware and technology for machine learning. NANOTECHNOLOGY 2021; 32:012002. [PMID: 32679577 PMCID: PMC11411818 DOI: 10.1088/1361-6528/aba70f] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
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Affiliation(s)
- Karl Berggren
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, United States of America
| | | | - Dmitri B Strukov
- Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States of America
| | - Hao Jiang
- School of Engineering & Applied Science Yale University, CT, United States of America
| | | | | | - Martin Salinga
- Institut für Materialphysik, Westfälische Wilhelms-Universität Münster, Germany
| | - John R Erickson
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
| | - Shuang Pi
- Lam Research, Fremont, CA, United States of America
| | - Feng Xiong
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States of America
| | - Peng Lin
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Chen
- School of information science and technology, Fudan University, Shanghai, People's Republic of China
| | - Shisheng Xiong
- School of information science and technology, Fudan University, Shanghai, People's Republic of China
| | - Brian D Hoskins
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Matthew W Daniels
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Advait Madhavan
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
- Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, United States of America
| | - James A Liddle
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Jabez J McClelland
- Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Yuchao Yang
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China
| | - Jennifer Rupp
- Department of Materials Science and Engineering and Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
- Electrochemical Materials, ETHZ Department of Materials, Hönggerbergring 64, Zürich 8093, Switzerland
| | - Stephen S Nonnenmann
- Department of Mechanical & Industrial Engineering, University of Massachusetts-Amherst, MA, United States of America
| | - Kwang-Ting Cheng
- School of Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People's Republic of China
| | - Nanbo Gong
- IBM T J Watson Research Center, Yorktown Heights, NY 10598, United States of America
| | - Miguel Angel Lastras-Montaño
- Instituto de Investigación en Comunicación Óptica, Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, México
| | - A Alec Talin
- Sandia National Laboratories, Livermore, CA 94551, United States of America
| | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, California, United States of America
| | - Bhavin J Shastri
- Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston ON KL7 3N6, Canada
| | - Thomas Ferreira de Lima
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, United States of America
| | - Paul Prucnal
- Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, United States of America
| | - Alexander N Tait
- Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Boulder, CO 80305, United States of America
| | - Yichen Shen
- Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America
| | - Huaiyu Meng
- Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America
| | - Charles Roques-Carmes
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Zengguang Cheng
- Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
| | - Harish Bhaskaran
- Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, United States of America
| | - Han Wang
- University of Southern California, Los Angeles, CA 90089, United States of America
| | - Jeffrey M Shainline
- Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Boulder, CO 80305, United States of America
| | - Kenneth Segall
- Department of Physics and Astronomy, Colgate University, NY 13346, United States of America
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, United States of America
| | - Kaushik Roy
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States of America
| | - Suman Datta
- University of Notre Dame, Notre Dame, IN 46556, United States of America
| | - Arijit Raychowdhury
- Georgia Institute of Technology, Atlanta, GA 30332, United States of America
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41
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Yanilkin I, Mohammed W, Gumarov A, Kiiamov A, Yusupov R, Tagirov L. Synthesis, Characterization, and Magnetoresistive Properties of the Epitaxial Pd 0.96Fe 0.04/VN/Pd 0.92Fe 0.08 Superconducting Spin-Valve Heterostructure. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 11:E64. [PMID: 33383847 PMCID: PMC7824622 DOI: 10.3390/nano11010064] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/15/2020] [Accepted: 12/25/2020] [Indexed: 12/03/2022]
Abstract
A thin-film superconductor(S)/ferromagnet(F) F1/S/F2-type Pd0.96Fe0.04(20 nm)/VN(30 nm)/Pd0.92Fe0.08(12 nm) heteroepitaxial structure was synthesized on (001)-oriented single-crystal MgO substrate utilizing a combination of the reactive magnetron sputtering and the molecular-beam epitaxy techniques in ultrahigh vacuum conditions. The reference VN film, Pd0.96Fe0.04/VN, and VN/Pd0.92Fe0.08 bilayers were grown in one run with the target sample. In-situ low-energy electron diffraction and ex-situ X-ray diffraction investigations approved that all the Pd1-xFex and VN layers in the series grew epitaxial in a cube-on-cube mode. Electric resistance measurements demonstrated sharp transitions to the superconducting state with the critical temperature reducing gradually from 7.7 to 5.4 K in the sequence of the VN film, Pd0.96Fe0.04/VN, VN/Pd0.92Fe0.08, and Pd0.96Fe0.04/VN/Pd0.92Fe0.08 heterostructures due to the superconductor/ferromagnet proximity effect. Transition width increased in the same sequence from 21 to 40 mK. Magnetoresistance studies of the trilayer Pd0.96Fe0.04/VN/Pd0.92Fe0.08 sample revealed a superconducting spin-valve effect upon switching between the parallel and antiparallel magnetic configurations, and anomalies associated with the magnetic moment reversals of the ferromagnetic Pd0.92Fe0.08 and Pd0.96Fe0.04 alloy layers. The moderate critical temperature suppression and manifestations of superconducting spin-valve properties make this kind of material promising for superconducting spintronics applications.
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Affiliation(s)
- Igor Yanilkin
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
| | - Wael Mohammed
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
- Department of Physics, Faculty of Science, Minia University, Minia 61519, Egypt
| | - Amir Gumarov
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
- Zavoisky Physical-Technical Institute, FRC Kazan Scientific Centre of RAS, 420029 Kazan, Russia
| | - Airat Kiiamov
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
| | - Roman Yusupov
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
| | - Lenar Tagirov
- Institute of Physics, Kazan Federal University, Kremlyovskaya Str. 18, 420008 Kazan, Russia; (I.Y.); (W.M.); (A.G.); (A.K.)
- Zavoisky Physical-Technical Institute, FRC Kazan Scientific Centre of RAS, 420029 Kazan, Russia
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Magnetotransport Properties of Ferromagnetic Nanoparticles in a Semiconductor Matrix Studied by Precise Size-Selective Cluster Ion Beam Deposition. NANOMATERIALS 2020; 10:nano10112192. [PMID: 33153149 PMCID: PMC7693789 DOI: 10.3390/nano10112192] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/27/2020] [Accepted: 10/30/2020] [Indexed: 01/26/2023]
Abstract
The combination of magnetic and semiconducting properties in one material system has great potential for integration of emerging spintronics with conventional semiconductor technology. One standard route for the synthesis of magnetic semiconductors is doping of semiconductors with magnetic atoms. In many semiconductor–magnetic–dopant systems, the magnetic atoms form precipitates within the semiconducting matrix. An alternative and controlled way to realize such nanocomposite materials is the assembly by co-deposition of size-selected cluster ions and a semiconductor. Here we follow the latter approach to demonstrate that this fabrication route can be used to independently study the influence of cluster concentration and cluster size on magneto-transport properties. In this case we study Fe clusters composed of approximately 500 or 1000 atoms soft-landed into a thermally evaporated amorphous Ge matrix. The analysis of field and temperature dependent transport shows that tunneling processes affected by Coulomb blockade dominate at low temperatures. The nanocomposites show saturating tunneling magnetoresistance, additionally superimposed by at least one other effect not saturating upon the maximum applied field of 6 T. The nanocomposites’ resistivity and the observed tunneling magnetoresistance depend exponentially on the average distance between cluster surfaces. On the contrary, there is no notable influence of the cluster size on the tunneling magnetoresistance.
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Tao J, Sarkar D, Kale S, Singh PK, Kapadia R. Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering. NANO LETTERS 2020; 20:7793-7801. [PMID: 32960612 DOI: 10.1021/acs.nanolett.0c03548] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As one of the key neuronal activities associated with memory in the human brain, memory consolidation is the process of the transition of short-term memory (STM) to long-term memory (LTM), which transforms an external stimulus to permanently stored information. Here, we report the emulation of this complex synaptic function, consolidation of STM to LTM, in a single-crystal indium phosphide (InP) field effect transistor (FET)-based artificial synapse. This behavior is achieved via the dielectric band and charge trap lifetime engineering in a dielectric gate heterostructure of aluminum oxide and titanium oxide. We analyze the behavior of these complex synaptic functions by engineering a variety of action potential parameters, and the devices exhibit good endurance, long retention time (>105 s), and high uniformity. Uniquely, this approach utilizes growth and device fabrication techniques which are scalable and back-end CMOS compatible, making this InP synaptic device a potential building block for neuromorphic computing.
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Affiliation(s)
- Jun Tao
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Debarghya Sarkar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Salil Kale
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Prakhar Kumar Singh
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Rehan Kapadia
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
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44
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Bakurskiy S, Kupriyanov M, Klenov NV, Soloviev I, Schegolev A, Morari R, Khaydukov Y, Sidorenko AS. Controlling the proximity effect in a Co/Nb multilayer: the properties of electronic transport. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2020; 11:1336-1345. [PMID: 32974112 PMCID: PMC7492690 DOI: 10.3762/bjnano.11.118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 08/07/2020] [Indexed: 06/11/2023]
Abstract
We present both theoretical and experimental investigations of the proximity effect in a stack-like superconductor/ferromagnetic (S/F) superlattice, where ferromagnetic layers with different thicknesses and coercive fields are made of Co. Calculations based on the Usadel equations allow us to find the conditions at which switching from the parallel to the antiparallel alignment of the neighboring F-layers leads to a significant change of the superconducting order parameter in superconductive thin films. We experimentally study the transport properties of a lithographically patterned Nb/Co multilayer. We observe that the resistive transition of the multilayer structure has multiple steps, which we attribute to the transition of individual superconductive layers with the critical temperature, T c, depending on the local magnetization orientation of the neighboring F-layers. We argue that such superlattices can be used as tunable kinetic inductors designed for artificial neural networks representing the information in a "current domain".
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Affiliation(s)
- Sergey Bakurskiy
- Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
| | - Mikhail Kupriyanov
- Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
| | - Nikolay V Klenov
- Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
- Lomonosov Moscow State University, Physics Department, Moscow, 119991, Russia
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Igor Soloviev
- Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
| | - Andrey Schegolev
- Lomonosov Moscow State University, Physics Department, Moscow, 119991, Russia
- Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
| | - Roman Morari
- Moscow Institute of Physics and Technology, State University, Dolgoprudny, Moscow Region 141700, Russia
- Institute of Electronic Engineering and Nanotechnologies ASM, MD2028 Kishinev, Moldova
| | - Yury Khaydukov
- Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia
- Max-Planck-Institut für Festkörperforschung, Heisenbergstraße 1, D-70569 Stuttgart, Germany
| | - Anatoli S Sidorenko
- Institute of Electronic Engineering and Nanotechnologies ASM, MD2028 Kishinev, Moldova
- Laboratory of Functional Nanostructures, Orel State University named after I.S. Turgenev, 302026, Orel, Russia
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45
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Pan X, Jin T, Gao J, Han C, Shi Y, Chen W. Stimuli-Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2001504. [PMID: 32734644 DOI: 10.1002/smll.202001504] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 05/27/2020] [Indexed: 06/11/2023]
Abstract
Brain-inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli-enabled synapses will be important for future neuromorphic computing applications. In this Review, state-of-the-art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox-based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.
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Affiliation(s)
- Xuan Pan
- SZU-NUS Collaborative Innovation Center for Optoelectronic Science & Technology, International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, China
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, 117542, Singapore
| | - Tengyu Jin
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, 117542, Singapore
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, P. R. China
| | - Jing Gao
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, 117542, Singapore
| | - Cheng Han
- SZU-NUS Collaborative Innovation Center for Optoelectronic Science & Technology, International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, China
| | - Yumeng Shi
- SZU-NUS Collaborative Innovation Center for Optoelectronic Science & Technology, International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, China
| | - Wei Chen
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, 117542, Singapore
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, P. R. China
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
- National University of Singapore (Suzhou) Research Institute, 377 Lin Quan Street, Suzhou Industrial Park, Jiangsu, 215123, China
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46
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Wang TY, Meng JL, Rao MY, He ZY, Chen L, Zhu H, Sun QQ, Ding SJ, Bao WZ, Zhou P, Zhang DW. Three-Dimensional Nanoscale Flexible Memristor Networks with Ultralow Power for Information Transmission and Processing Application. NANO LETTERS 2020; 20:4111-4120. [PMID: 32186388 DOI: 10.1021/acs.nanolett.9b05271] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.
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Affiliation(s)
- Tian-Yu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Ming-Yi Rao
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Zhen-Yu He
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Shi-Jin Ding
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Wen-Zhong Bao
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Peng Zhou
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
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47
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Park HL, Lee Y, Kim N, Seo DG, Go GT, Lee TW. Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1903558. [PMID: 31559670 DOI: 10.1002/adma.201903558] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 07/10/2019] [Indexed: 05/08/2023]
Abstract
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
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Affiliation(s)
- Hea-Lim Park
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Yeongjun Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- BK21 PLUS SNU Materials Division for Educating Creative Global Leaders, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Naryung Kim
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Gyeong-Tak Go
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- BK21 PLUS SNU Materials Division for Educating Creative Global Leaders, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- Institute of Engineering Research Research Institute of Advanced Materials, Nano Systems Institute (NSI), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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48
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Wang P, Nasir ME, Krasavin AV, Dickson W, Zayats AV. Optoelectronic Synapses Based on Hot-Electron-Induced Chemical Processes. NANO LETTERS 2020; 20:1536-1541. [PMID: 32013449 DOI: 10.1021/acs.nanolett.9b03871] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Highly efficient information processing in the brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here, we have developed an artificial synapse with both electrical and optical memory effects using chemical transformations in plasmonic tunnel junctions. In an electronic implementation, the electrons tunneled into plasmonic nanorods under a low bias voltage are harvested to write information into the tunnel junctions via hot-electron-mediated chemical reactions with the environment. In an optical realization, the information can be written by an external light illumination to excite hot electrons in the plasmonic nanorods. The stored information is nonvolatile and can be read either electrically or optically by measuring the resistance or inelastic-tunneling-induced light emission, respectively. The described architecture provides a high density (∼1010 cm-2) of memristive optoelectronic devices which can be used as multilevel nonvolatile memory, logic units, or artificial synapses in future electronic, optoelectronic, and artificial neural networks.
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Affiliation(s)
- Pan Wang
- Department of Physics and London Centre for Nanotechnology, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Mazhar E Nasir
- Department of Physics and London Centre for Nanotechnology, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Alexey V Krasavin
- Department of Physics and London Centre for Nanotechnology, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Wayne Dickson
- Department of Physics and London Centre for Nanotechnology, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Anatoly V Zayats
- Department of Physics and London Centre for Nanotechnology, King's College London, Strand, London WC2R 2LS, United Kingdom
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49
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Li J, Ge C, Du J, Wang C, Yang G, Jin K. Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1905764. [PMID: 31850652 DOI: 10.1002/adma.201905764] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/19/2019] [Indexed: 05/22/2023]
Abstract
Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.
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Affiliation(s)
- Jiankun 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
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai, 200241, China
| | - Jianyu Du
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, China
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50
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Synaptic weighting in single flux quantum neuromorphic computing. Sci Rep 2020; 10:934. [PMID: 31969626 PMCID: PMC6976664 DOI: 10.1038/s41598-020-57892-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/30/2019] [Indexed: 11/08/2022] Open
Abstract
Josephson junctions act as a natural spiking neuron-like device for neuromorphic computing. By leveraging the advances recently demonstrated in digital single flux quantum (SFQ) circuits and using recently demonstrated magnetic Josephson junction (MJJ) synaptic circuits, there is potential to make rapid progress in SFQ-based neuromorphic computing. Here we demonstrate the basic functionality of a synaptic circuit design that takes advantage of the adjustable critical current demonstrated in MJJs and implement a synaptic weighting element. The devices were fabricated with a restively shunted Nb/AlOx-Al/Nb process that did not include MJJs. Instead, the MJJ functionality was tested by making multiple circuits and varying the critical current, but not the external shunt resistance, of the oxide Josephson junction that represents the MJJ. Experimental measurements and simulations of the fabricated circuits are in good agreement.
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