1
|
Yuan Y, Patel RK, Banik S, Reta TB, Bisht RS, Fong DD, Sankaranarayanan SKRS, Ramanathan S. Proton Conducting Neuromorphic Materials and Devices. Chem Rev 2024; 124:9733-9784. [PMID: 39038231 DOI: 10.1021/acs.chemrev.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.
Collapse
Affiliation(s)
- Yifan Yuan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Ranjan Kumar Patel
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Suvo Banik
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Tadesse Billo Reta
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Ravindra Singh Bisht
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| | - Dillon D Fong
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Subramanian K R S Sankaranarayanan
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Shriram Ramanathan
- Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States
| |
Collapse
|
2
|
Li R, Yue Z, Luan H, Dong Y, Chen X, Gu M. Multimodal Artificial Synapses for Neuromorphic Application. RESEARCH (WASHINGTON, D.C.) 2024; 7:0427. [PMID: 39161534 PMCID: PMC11331013 DOI: 10.34133/research.0427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024]
Abstract
The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.
Collapse
Affiliation(s)
- Runze Li
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
- Institute of Photonic Chips,
University of Shanghai for Science and Technology, Shanghai 200093, China
- Zhangjiang Laboratory, Pudong, Shanghai 201210, China
| | - Zengji Yue
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Haitao Luan
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yibo Dong
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xi Chen
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Min Gu
- School of Artificial Intelligence Science and Technology,
University of Shanghai for Science and Technology, Shanghai 200093, China
| |
Collapse
|
3
|
Li M, Li M, An JS, An H, Kim DH, Lee YH, Park KK, Kim TW. Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability. ACS APPLIED MATERIALS & INTERFACES 2024; 16:42380-42391. [PMID: 39090057 DOI: 10.1021/acsami.4c04957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).
Collapse
Affiliation(s)
- Mingjun Li
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Ming Li
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Jun Seop An
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Haoqun An
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Dae Hun Kim
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Yong Hun Lee
- Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Kwan Kyu Park
- Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Tae Whan Kim
- Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| |
Collapse
|
4
|
Guo F, Liu Y, Zhang M, Yu W, Li S, Zhang B, Hu B, Li S, Sun A, Jiang J, Hao L. VO 2/MoO 3 Heterojunctions Artificial Optoelectronic Synapse Devices for Near-Infrared Optical Communication. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310767. [PMID: 38456772 DOI: 10.1002/smll.202310767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/23/2024] [Indexed: 03/09/2024]
Abstract
Artificial optoelectronic synapses (OES) have attracted extensive attention in brain-inspired information processing and neuromorphic computing. However, OES at near-infrared wavelengths have rarely been reported, seriously limiting the application in modern optical communication. Herein, high-performance near-infrared OES devices based on VO2/MoO3 heterojunctions are presented. The textured MoO3 films are deposited on the sputtered VO2 film by using the glancing-angle deposition technique to form a heterojunction device. Through tuning the oxygen defects in the VO2 film, the fabricated VO2/MoO3 heterojunction exhibits versatile electrical synaptic functions. Benefiting from the highly efficient light harvesting and the unique interface effect, the photonic synaptic characteristics, mainly including the short/long-term plasticity and learning experience behavior are successfully realized at the O (1342 nm) and C (1550 nm) optical communication wavebands. Moreover, a single OES device can output messages accurately by converting light signals of the Morse code to distinct synaptic currents. More importantly, a 3 × 3 artificial OES array is constructed to demonstrate the impressive image perceiving and learning capabilities. This work not only indicates the feasibility of defect and interface engineering in modulating the synaptic plasticity of OES devices, but also provides effective strategies to develop advanced artificial neuromorphic visual systems for next-generation optical communication systems.
Collapse
Affiliation(s)
- Fuhai Guo
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Yunjie Liu
- College of Science, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Mingcong Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Weizhuo Yu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Siqi Li
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bo Zhang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Bing Hu
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Shuangshuang Li
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Ankai Sun
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Jianyu Jiang
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| | - Lanzhong Hao
- School of Materials Science and Engineering, China University of Petroleum, Qingdao, Shandong, 266580, China
| |
Collapse
|
5
|
Leng K, Wan Y, Fu Y, Wang L, Wang Q. Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309945. [PMID: 38400705 DOI: 10.1002/smll.202309945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Indexed: 02/25/2024]
Abstract
In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.
Collapse
Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yu Wan
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yao Fu
- Department of Materials, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Li Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Qisheng Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| |
Collapse
|
6
|
Oh J, Park S, Lee SH, Kim S, Lee H, Lee C, Hong W, Cha J, Kang M, Jin JH, Im SG, Kim MJ, Choi S. Ultrathin All-Solid-State MoS 2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic-Inorganic Hybrid Film. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308847. [PMID: 38566434 PMCID: PMC11187882 DOI: 10.1002/advs.202308847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/24/2024] [Indexed: 04/04/2024]
Abstract
Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.
Collapse
Affiliation(s)
- Jungyeop Oh
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seohak Park
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sang Hun Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials EngineeringSejong University209 Neungdong‐ro, Gwangjin‐guSeoul05006Republic of Korea
| | - Hyeonji Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Woonggi Hong
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Jun‐Hwe Cha
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Mingu Kang
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jun Hyup Jin
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Min Ju Kim
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung‐Yool Choi
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| |
Collapse
|
7
|
Song HW, Moon D, Won Y, Cha YK, Yoo J, Park TH, Oh JH. A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices. SCIENCE ADVANCES 2024; 10:eadl2882. [PMID: 38781346 PMCID: PMC11114221 DOI: 10.1126/sciadv.adl2882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.
Collapse
Affiliation(s)
- Hyun Woo Song
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Dongseok Moon
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Yousang Won
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Yeon Kyung Cha
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Jin Yoo
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Tai Hyun Park
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Joon Hak Oh
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea
| |
Collapse
|
8
|
Li Y, Cai W, Tao R, Shuai W, Rao J, Chang C, Lu X, Ning H. Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High- k Oxide Composite Dielectric. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19271-19282. [PMID: 38591357 DOI: 10.1021/acsami.4c02880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.
Collapse
Affiliation(s)
- Yushan Li
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Wei Cai
- Jihua Laboratory, Foshan, Guangzhou 528000, China
| | - Ruiqiang Tao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Wentao Shuai
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Jingjing Rao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Cheng Chang
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Xubing Lu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Honglong Ning
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China
| |
Collapse
|
9
|
Ghafoor F, Kim H, Ghafoor B, Rehman S, Asghar Khan M, Aziz J, Rabeel M, Faheem Maqsood M, Dastgeer G, Lee MJ, Farooq Khan M, Kim DK. Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing. J Colloid Interface Sci 2024; 659:1-10. [PMID: 38157721 DOI: 10.1016/j.jcis.2023.12.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/29/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/-0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.
Collapse
Affiliation(s)
- Faisal Ghafoor
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
| | - Honggyun Kim
- Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Bilal Ghafoor
- PPGE3M, Federal University of Rio Grande do Sul, Porto Alegre /RS, Brazil
| | - Shania Rehman
- Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
| | | | - Jamal Aziz
- Chair of Smart Sensor Systems, University of Wuppertal, Wuppertal, Germany
| | - Muhammad Rabeel
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Faheem Maqsood
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, 05006 Seoul, Republic of Korea
| | - Ghulam Dastgeer
- Department of Physics and Astronomy, Sejong University, Seoul 05006, Korea
| | - Myoung-Jae Lee
- Institute of Conversion Daegu Gyeongbuk Institute of Science and Technology (DGIST)., Daegu 42988, Republic of Korea.
| | - Muhammad Farooq Khan
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.
| | - Deok-Kee Kim
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea; Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
| |
Collapse
|
10
|
Bag SP, Lee S, Song J, Kim J. Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review. BIOSENSORS 2024; 14:150. [PMID: 38534257 DOI: 10.3390/bios14030150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.
Collapse
Affiliation(s)
- Sankar Prasad Bag
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Suyoung Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jaeyoon Song
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jinsink Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| |
Collapse
|
11
|
Jiang S, Peng L, Li L, Dai Q, Pei M, Wu C, Su J, Gu D, Zhang H, Guo H, Qiu J, Li Y. Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations. J Phys Chem Lett 2024; 15:2301-2310. [PMID: 38386516 DOI: 10.1021/acs.jpclett.4c00284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The brain's function can be dynamically reconfigured through a unified neuron-synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric-electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.
Collapse
Affiliation(s)
- Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Lichao Peng
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Longfei Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Qinyong Dai
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Chaoran Wu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jian Su
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Ding Gu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Han Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Huafei Guo
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jianhua Qiu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| |
Collapse
|
12
|
Kwak H, Kim N, Jeon S, Kim S, Woo J. Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing. NANO CONVERGENCE 2024; 11:9. [PMID: 38416323 PMCID: PMC10902254 DOI: 10.1186/s40580-024-00415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024]
Abstract
Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.
Collapse
Affiliation(s)
- Hyunjeong Kwak
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Nayeon Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seonuk Jeon
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Seyoung Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
| | - Jiyong Woo
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, South Korea.
| |
Collapse
|
13
|
Yu C, Li S, Pan Z, Liu Y, Wang Y, Zhou S, Gao Z, Tian H, Jiang K, Wang Y, Zhang J. Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor. NANO LETTERS 2024; 24:1620-1628. [PMID: 38277130 DOI: 10.1021/acs.nanolett.3c04193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.
Collapse
Affiliation(s)
- Chenglin Yu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Shaorui Li
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhoujie Pan
- XingJian College, Tsinghua University, Beijing 100084, China
| | - Yanming Liu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yongchao Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - Siyi Zhou
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Zhiting Gao
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
| | - He Tian
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Kaili Jiang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Tsinghua-Foxconn Nanotechnology Research Center, Department of Physics, Tsinghua University, Beijing 100084, China
| | - Yayu Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
| | - Jinsong Zhang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Hefei National Laboratory, Hefei 230088, China
| |
Collapse
|
14
|
Ma H, Fang H, Xie X, Liu Y, Tian H, Chai Y. Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception. NANO-MICRO LETTERS 2024; 16:104. [PMID: 38300424 PMCID: PMC10834395 DOI: 10.1007/s40820-024-01330-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.
Collapse
Affiliation(s)
- Hailong Ma
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Huajing Fang
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Xinxing Xie
- Center for Advancing Materials Performance From the Nanoscale (CAMP-Nano), State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Yanming Liu
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - He Tian
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, People's Republic of China.
| |
Collapse
|
15
|
Sunny MM, Thamankar R. Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO 3 based two-terminal devices - efficient way to control the synaptic amplification. RSC Adv 2024; 14:2518-2528. [PMID: 38226148 PMCID: PMC10788777 DOI: 10.1039/d3ra07757h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024] Open
Abstract
Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.
Collapse
Affiliation(s)
- Meenu Maria Sunny
- Department of Physics, Vellore Institute of Technology Vellore TN India
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
| | - R Thamankar
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
| |
Collapse
|
16
|
Obaidulla SM, Supina A, Kamal S, Khan Y, Kralj M. van der Waals 2D transition metal dichalcogenide/organic hybridized heterostructures: recent breakthroughs and emerging prospects of the device. NANOSCALE HORIZONS 2023; 9:44-92. [PMID: 37902087 DOI: 10.1039/d3nh00310h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
The near-atomic thickness and organic molecular systems, including organic semiconductors and polymer-enabled hybrid heterostructures, of two-dimensional transition metal dichalcogenides (2D-TMDs) can modulate their optoelectronic and transport properties outstandingly. In this review, the current understanding and mechanism of the most recent and significant breakthrough of novel interlayer exciton emission and its modulation by harnessing the band energy alignment between TMDs and organic semiconductors in a TMD/organic (TMDO) hybrid heterostructure are demonstrated. The review encompasses up-to-date device demonstrations, including field-effect transistors, detectors, phototransistors, and photo-switchable superlattices. An exploration of distinct traits in 2D-TMDs and organic semiconductors delves into the applications of TMDO hybrid heterostructures. This review provides insights into the synthesis of 2D-TMDs and organic layers, covering fabrication techniques and challenges. Band bending and charge transfer via band energy alignment are explored from both structural and molecular orbital perspectives. The progress in emission modulation, including charge transfer, energy transfer, doping, defect healing, and phase engineering, is presented. The recent advancements in 2D-TMDO-based optoelectronic synaptic devices, including various 2D-TMDs and organic materials for neuromorphic applications are discussed. The section assesses their compatibility for synaptic devices, revisits the operating principles, and highlights the recent device demonstrations. Existing challenges and potential solutions are discussed. Finally, the review concludes by outlining the current challenges that span from synthesis intricacies to device applications, and by offering an outlook on the evolving field of emerging TMDO heterostructures.
Collapse
Affiliation(s)
- Sk Md Obaidulla
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Department of Condensed Matter and Materials Physics, S. N. Bose National Centre for Basic Sciences, Sector III, Block JD, Salt Lake, Kolkata 700106, India
| | - Antonio Supina
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
- Chair of Physics, Montanuniversität Leoben, Franz Josef Strasse 18, 8700 Leoben, Austria
| | - Sherif Kamal
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
| | - Yahya Khan
- Department of Physics, Karakoram International university (KIU), Gilgit 15100, Pakistan
| | - Marko Kralj
- Center of Excellence for Advanced Materials and Sensing Devices, Institute of Physics, Bijenička Cesta 46, HR-10000 Zagreb, Croatia.
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Zhang C, Ning J, Wang D, Zhang J, Hao Y. A review on advanced band-structure engineering with dynamic control for nonvolatile memory based 2D transistors. NANOTECHNOLOGY 2023; 35:042001. [PMID: 37524059 DOI: 10.1088/1361-6528/acebf4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
With advancements in information technology, an enormous amount of data is being generated that must be quickly accessible. However, conventional Si memory cells are approaching their physical limits and will be unable to meet the requirements of intense applications in the future. Notably, 2D atomically thin materials have demonstrated multiple novel physical and chemical properties that can be used to investigate next-generation electronic devices and breakthrough physical limits to continue Moore's law. Band structure is an important semiconductor parameter that determines their electrical and optical properties. In particular, 2D materials have highly tunable bandgaps and Fermi levels that can be achieved through band structure engineering methods such as heterostructure, substrate engineering, chemical doping, intercalation, and electrostatic doping. In particular, dynamic control of band structure engineering can be used in recent advancements in 2D devices to realize nonvolatile storage performance. This study examines recent advancements in 2D memory devices that utilize band structure engineering. The operational mechanisms and memory characteristics are described for each band structure engineering method. Band structure engineering provides a platform for developing new structures and realizing superior performance with respect to nonvolatile memory.
Collapse
Affiliation(s)
- Chi Zhang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Jing Ning
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Dong Wang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
- Xidian-Wuhu Research Institute, Wuhu 241000, People's Republic of China
| | - Jincheng Zhang
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| | - Yue Hao
- The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Xidian University, Xi'an 710071, People's Republic of China
- Shaanxi Joint Key Laboratory of Graphene, Xidian University, Xi'an 710071, People's Republic of China
| |
Collapse
|
19
|
Tran DM, Son JW, Ju TS, Hwang C, Park BH. Dopamine-Regulated Plasticity in MoO 3 Synaptic Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:49329-49337. [PMID: 37819637 DOI: 10.1021/acsami.3c06866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Field-effect transistor-based biosensors have gained increasing interest due to their reactive surface to external stimuli and the adaptive feedback required for advanced sensing platforms in biohybrid neural interfaces. However, complex probing methods for surface functionalization remain a challenge that limits the industrial implementation of such devices. Herein, a simple, label-free biosensor based on molybdenum oxide (MoO3) with dopamine-regulated plasticity is demonstrated. Dopamine oxidation facilitated locally at the channel surface initiates a charge transfer mechanism between the molecule and the oxide, altering the channel conductance and successfully emulating the tunable synaptic weight by neurotransmitter activity. The oxygen level of the channel is shown to heavily affect the device's electrochemical properties, shifting from a nonreactive metallic characteristic to highly responsive semiconducting behavior. Controllable responsivity is achieved by optimizing the channel's dimension, which allows the devices to operate in wide ranges of dopamine concentration, from 100 nM to sub-mM levels, with excellent selectivity compared with K+, Na+, and Ca2+.
Collapse
Affiliation(s)
- Duc Minh Tran
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
| | - Jong Wan Son
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Tae-Seong Ju
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Chanyong Hwang
- Quantum Spin Team, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea
| | - Bae Ho Park
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul 05029, Republic of Korea
| |
Collapse
|
20
|
Xu H, Shang D, Luo Q, An J, Li Y, Wu S, Yao Z, Zhang W, Xu X, Dou C, Jiang H, Pan L, Zhang X, Wang M, Wang Z, Tang J, Liu Q, Liu M. A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing. Nat Commun 2023; 14:6385. [PMID: 37821427 PMCID: PMC10567726 DOI: 10.1038/s41467-023-42172-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023] Open
Abstract
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.
Collapse
Affiliation(s)
- Han Xu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Dashan Shang
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Qing Luo
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junjie An
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Yue Li
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuyu Wu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Yao
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Woyu Zhang
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoxin Xu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chunmeng Dou
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Jiang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Liyang Pan
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Xumeng Zhang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Ming Wang
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, Hong Kong
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
| | - Qi Liu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China.
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
| | - Ming Liu
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100049, China
- Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| |
Collapse
|
21
|
Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
Collapse
Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| |
Collapse
|
22
|
Huang M, Schwacke M, Onen M, Del Alamo J, Li J, Yildiz B. Electrochemical Ionic Synapses: Progress and Perspectives. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205169. [PMID: 36300807 DOI: 10.1002/adma.202205169] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.
Collapse
Affiliation(s)
- Mantao Huang
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Miranda Schwacke
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Murat Onen
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jesús Del Alamo
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ju Li
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bilge Yildiz
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| |
Collapse
|
23
|
Liu G, Wang W, Guo Z, Jia X, Zhao Z, Zhou Z, Niu J, Duan G, Yan X. Silicon based Bi 0.9La 0.1FeO 3 ferroelectric tunnel junction memristor for convolutional neural network application. NANOSCALE 2023; 15:13009-13017. [PMID: 37485606 DOI: 10.1039/d3nr00510k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.
Collapse
Affiliation(s)
- Gongjie Liu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Wei Wang
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenqiang Guo
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaotong Jia
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhen Zhao
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenyu Zhou
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Jiangzhen Niu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Guojun Duan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaobing Yan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| |
Collapse
|
24
|
Ismail M, Rasheed M, Mahata C, Kang M, Kim S. Mimicking biological synapses with a-HfSiO x-based memristor: implications for artificial intelligence and memory applications. NANO CONVERGENCE 2023; 10:33. [PMID: 37428275 DOI: 10.1186/s40580-023-00380-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023]
Abstract
Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiOx-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiOx/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (104 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiOx-based memristors have great potential for use in multilevel memory and neuromorphic computing systems.
Collapse
Affiliation(s)
- Muhammad Ismail
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Maria Rasheed
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Chandreswar Mahata
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea
| | - Myounggon Kang
- Department of Electronics Engineering, Korea National University of Transportation, Chungju- si, 27469, Republic of Korea.
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, Republic of Korea.
| |
Collapse
|
25
|
Shi J, Kang S, Feng J, Fan J, Xue S, Cai G, Zhao JS. Evaluating charge-type of polyelectrolyte as dielectric layer in memristor and synapse emulation. NANOSCALE HORIZONS 2023; 8:509-515. [PMID: 36757200 DOI: 10.1039/d2nh00524g] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Based on credible advantages, organic materials have received more and more attention in memristor and synapse emulation. In particular, an implementation of the ionic pathway as a dielectric layer is essential for organic materials used as building blocks of memristor and artificial synaptic devices. Herein, we describe an evaluation of the use of positive and negative polyelectrolytes as dielectric layers for a memristor with calcium ion (Ca2+) doping. The device based on a negative polyelectrolyte shows the potential to obtain an excellent resistive switching performance and synapse functionality, especially in the transformation behaviours from short-term plasticity (STP) to long-term plasticity (LTP) in both the potentiation and depression processes, which were comparable to the perfomrmance obtained with a positive polyelectrolyte. The mechanism of electrical resistance transition and synaptic function can be attributed to the migration of the doped Ca2+ and the ionic functional groups of polyelectrolyte, which result in the formation and vanishing filament-like Ca2+ flux.
Collapse
Affiliation(s)
- Jingzhou Shi
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Shaohui Kang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jiang Feng
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jiaming Fan
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Song Xue
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Gangri Cai
- Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion, Department of Applied Chemistry, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| | - Jin Shi Zhao
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, No. 391, Binshui Xidao, Xiqing District, Tianjin, 300384, PR China.
| |
Collapse
|
26
|
Chen H, Li H, Ma T, Han S, Zhao Q. Biological function simulation in neuromorphic devices: from synapse and neuron to behavior. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2183712. [PMID: 36926202 PMCID: PMC10013381 DOI: 10.1080/14686996.2023.2183712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.
Collapse
Affiliation(s)
- Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| | - Huilin Li
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Ting Ma
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Shuangshuang Han
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| |
Collapse
|
27
|
Cheng S, Zhong L, Yin J, Duan H, Xie Q, Luo W, Jie W. Controllable digital and analog resistive switching behavior of 2D layered WSe 2 nanosheets for neuromorphic computing. NANOSCALE 2023; 15:4801-4808. [PMID: 36779310 DOI: 10.1039/d2nr06580k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe2 nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 104. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe2 nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.
Collapse
Affiliation(s)
- Siqi Cheng
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Lun Zhong
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Jinxiang Yin
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Huan Duan
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| | - Qin Xie
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wenbo Luo
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wenjing Jie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
| |
Collapse
|
28
|
Shao H, Li Y, Yang W, He X, Wang L, Fu J, Fu M, Ling H, Gkoupidenis P, Yan F, Xie L, Huang W. A Reconfigurable Optoelectronic Synaptic Transistor with Stable Zr-CsPbI 3 Nanocrystals for Visuomorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208497. [PMID: 36620940 DOI: 10.1002/adma.202208497] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/19/2022] [Indexed: 06/17/2023]
Abstract
Reconfigurable phototransistor memory attracts considerable attention for adaptive visuomorphic computing, with highly efficient sensing, memory, and processing functions integrated onto a single device. However, developing reconfigurable phototransistor memory remains a challenge due to the lack of an all-optically controlled transition between short-term plasticity (STP) and long-term plasticity (LTP). Herein, an air-stable Zr-CsPbI3 perovskite nanocrystal (PNC)-based phototransistor memory is designed, which is capable of broadband photoresponses. Benefitting from the different electron capture ability of Zr-CsPbI3 PNCs to 650 and 405 nm light, an artificial synapse and non-volatile memory can be created on-demand and quickly reconfigured within a single device for specific purposes. Owing to the optically reconfigurable and wavelength-aware operation between STP and LTP modes, the integrated blue feature extraction and target recognition can be demonstrated in a homogeneous neuromorphic vision sensor array. This work suggests a new way in developing perovskite optoelectronic transistors for highly efficient in-sensor computing.
Collapse
Affiliation(s)
- He Shao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Yueqing Li
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Wei Yang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Xiang He
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Le Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Jingwei Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Mingyang Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Haifeng Ling
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
- Department of Molecular Electronics, Max Planck Institute for Polymer Research, 55128, Mainz, Germany
| | - Paschalis Gkoupidenis
- Department of Molecular Electronics, Max Planck Institute for Polymer Research, 55128, Mainz, Germany
| | - Feng Yan
- Department of Applied Physics, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, P. R. China
| | - Linghai Xie
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, P. R. China
- Frontiers Science Center for Flexible Electronics (FSCFE), MIIT Key Laboratory of Flexible Electronics (KLoFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, 710072, P. R. China
| |
Collapse
|
29
|
Zhang F, Li C, Li Z, Dong L, Zhao J. Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. MICROSYSTEMS & NANOENGINEERING 2023; 9:16. [PMID: 36817330 PMCID: PMC9935897 DOI: 10.1038/s41378-023-00487-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.
Collapse
Affiliation(s)
- Fanqing Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Chunyang Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Zhongyi Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong, China
| | - Jing Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Wang S, Liu X, Zhou P. The Road for 2D Semiconductors in the Silicon Age. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106886. [PMID: 34741478 DOI: 10.1002/adma.202106886] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.
Collapse
Affiliation(s)
- Shuiyuan Wang
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Xiaoxian Liu
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
- Frontier Institute of Chip and System, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, 200433, China
| |
Collapse
|
32
|
Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
Collapse
Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
| |
Collapse
|
33
|
Fu Y, Chan YT, Jiang YP, Chang KH, Wu HC, Lai CS, Wang JC. Polarity-Differentiated Dielectric Materials in Monolayer Graphene Charge-Regulated Field-Effect Transistors for an Artificial Reflex Arc and Pain-Modulation System of the Spinal Cord. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202059. [PMID: 35619163 DOI: 10.1002/adma.202202059] [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] [Received: 03/04/2022] [Revised: 04/28/2022] [Indexed: 06/15/2023]
Abstract
The nervous system is a vital part of organisms to survive and it endows them with remarkable abilities, such as perception, recognition, regulation, learning, and decision-making, by intertwining myriad neurons. To realize such outstanding efficacies and functions, many artificial devices and systems have been investigated to emulate the operating principles of the nervous system. Here, an artificial reflex arc (ARA) and artificial pain modulation system (APMS) are proposed to imitate the unconscious behaviors of the spinal cord. Gdx Oy - and Alx Oy -based charge-regulated field-effect transistors (CRFETs) with a monolayer graphene channel are fabricated and adopted as inhibitory and excitatory synapses, respectively, under the same pulse signals to mimic the biological reflex arc through a connection with a poly(vinylidene fluoride-co-trifluoroethylene)-based actuator. Additionally, a memristor is integrated with a CRFET as the interneuron to regulate the Dirac point by controlling the voltage drop on the graphene channel, analogous to the descending pain-inhibition system in the spinal cord, to prevent excessive pain perception. The proposed ARA and APMS provide a significant step forward to realizing the functions of the nervous system, giving promising potential for developing future intelligent alarm systems, neuroprosthetics, and neurorobotics.
Collapse
Affiliation(s)
- Yi Fu
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Ya-Ting Chan
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Yi-Pei Jiang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
| | - Jer-Chyi Wang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
| |
Collapse
|
34
|
Hu X, Liu K, Cai Y, Zang SQ, Zhai T. 2D Oxides for Electronics and Optoelectronics. SMALL SCIENCE 2022. [DOI: 10.1002/smsc.202200008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Xiaozong Hu
- Henan Key Laboratory of Crystalline Molecular Functional Materials Henan International Joint Laboratory of Tumor Theranostical Cluster Materials Green Catalysis Center, and College of Chemistry Zhengzhou University Zhengzhou 450001 P. R. China
| | - Kailang Liu
- State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 P. R. China
| | - Yongqing Cai
- Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Taipa 999078 Macau P. R. China
| | - Shuang-Quan Zang
- Henan Key Laboratory of Crystalline Molecular Functional Materials Henan International Joint Laboratory of Tumor Theranostical Cluster Materials Green Catalysis Center, and College of Chemistry Zhengzhou University Zhengzhou 450001 P. R. China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 P. R. China
| |
Collapse
|
35
|
Wu Z, Shi P, Xing R, Xing Y, Ge Y, Wei L, Wang D, Zhao L, Yan S, Chen Y. Quasi-two-dimensional α-molybdenum oxide thin film prepared by magnetron sputtering for neuromorphic computing. RSC Adv 2022; 12:17706-17714. [PMID: 35765332 PMCID: PMC9199084 DOI: 10.1039/d2ra02652j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/09/2022] [Indexed: 11/21/2022] Open
Abstract
Two-dimensional (2D) layered materials have attracted intensive attention in recent years due to their rich physical properties, and shown great promise due to their low power consumption and high integration density in integrated electronics. However, mostly limited to mechanical exfoliation, large scale preparation of the 2D materials for application is still challenging. Herein, quasi-2D α-molybdenum oxide (α-MoO3) thin film with an area larger than 100 cm2 was fabricated by magnetron sputtering, which is compatible with modern semiconductor industry. An all-solid-state synaptic transistor based on this α-MoO3 thin film is designed and fabricated. Interestingly, by proton intercalation/deintercalation, the α-MoO3 channel shows a reversible conductance modulation of about four orders. Several indispensable synaptic behaviors, such as potentiation/depression and short-term/long-term plasticity, are successfully demonstrated in this synaptic device. In addition, multilevel data storage has been achieved. Supervised pattern recognition with high recognition accuracy is demonstrated in a three-layer artificial neural network constructed on this α-MoO3 based synaptic transistor. This work can pave the way for large scale production of the α-MoO3 thin film for practical application in intelligent devices.
Collapse
Affiliation(s)
- Zhenfa Wu
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Peng Shi
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Ruofei Xing
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yuzhi Xing
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yufeng Ge
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Lin Wei
- School of Microelectronics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Dong Wang
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Le Zhao
- School of Electronic and Information Engineering, Qilu University of Technology Jinan 250353 China
| | - Shishen Yan
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| | - Yanxue Chen
- School of Physics, and State Key Laboratory of Crystal Materials, Shandong University Jinan 250100 China
| |
Collapse
|
36
|
Artificial neuromorphic cognitive skins based on distributed biaxially stretchable elastomeric synaptic transistors. Proc Natl Acad Sci U S A 2022; 119:e2204852119. [PMID: 35648822 DOI: 10.1073/pnas.2204852119] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SignificanceEnabling distributed neurologic and cognitive functions in soft deformable devices, such as robotics, wearables, skin prosthetics, bioelectronics, etc., represents a massive leap in their development. The results presented here reveal the device characteristics of the building block, i.e., a stretchable elastomeric synaptic transistor, its characteristics under various levels of biaxial strain, and performances of various stretchy distributed neuromorphic devices. The stretchable neuromorphic array of synaptic transistors and the neuromorphic imaging sensory skin enable platforms to create a wide range of soft devices and systems with implemented neuromorphic and cognitive functions, including artificial cognitive skins, wearable neuromorphic computing, artificial organs, neurorobotics, and skin prosthetics.
Collapse
|
37
|
Liu F, Deswal S, Christou A, Shojaei Baghini M, Chirila R, Shakthivel D, Chakraborty M, Dahiya R. Printed synaptic transistor-based electronic skin for robots to feel and learn. Sci Robot 2022; 7:eabl7286. [PMID: 35648845 DOI: 10.1126/scirobotics.abl7286] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.
Collapse
Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Mahdieh Shojaei Baghini
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Radu Chirila
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Dhayalan Shakthivel
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Moupali Chakraborty
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| |
Collapse
|
38
|
Yoon C, Oh G, Park BH. Ion-Movement-Based Synaptic Device for Brain-Inspired Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:1728. [PMID: 35630952 PMCID: PMC9148095 DOI: 10.3390/nano12101728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023]
Abstract
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.
Collapse
Affiliation(s)
| | | | - Bae Ho Park
- Division of Quantum Phases & Devices, Department of Physics, Konkuk University, Seoul 05029, Korea; (C.Y.); (G.O.)
| |
Collapse
|
39
|
Han S, Zhang R, Han L, Zhao C, Yan X, Dai M. An Antifatigue and Self-healable Ionic Polyurethane/Ionic Liquid Composite as the Channel Layer for A Low Energy Cost Synaptic Transistor. Eur Polym J 2022. [DOI: 10.1016/j.eurpolymj.2022.111292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
40
|
Ion-Driven Electrochemical Random-Access Memory-Based Synaptic Devices for Neuromorphic Computing Systems: A Mini-Review. MICROMACHINES 2022; 13:mi13030453. [PMID: 35334745 PMCID: PMC8950570 DOI: 10.3390/mi13030453] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 12/10/2022]
Abstract
To enhance the computing efficiency in a neuromorphic architecture, it is important to develop suitable memory devices that can emulate the role of biological synapses. More specifically, not only are multiple conductance states needed to be achieved in the memory but each state is also analogously adjusted by consecutive identical pulses. Recently, electrochemical random-access memory (ECRAM) has been dedicatedly designed to realize the desired synaptic characteristics. Electric-field-driven ion motion through various electrolytes enables the conductance of the ECRAM to be analogously modulated, resulting in a linear and symmetric response. Therefore, the aim of this study is to review recent advances in ECRAM technology from the material and device engineering perspectives. Since controllable mobile ions play an important role in achieving synaptic behavior, the prospect and challenges of ECRAM devices classified according to mobile ion species are discussed.
Collapse
|
41
|
Monalisha P, Kumar APS, Wang XR, Piramanayagam SN. Emulation of Synaptic Plasticity on a Cobalt-Based Synaptic Transistor for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:11864-11872. [PMID: 35229606 DOI: 10.1021/acsami.1c19916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements─such as synaptic devices and neurons─are crucial. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.
Collapse
Affiliation(s)
- P Monalisha
- Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Anil P S Kumar
- Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Xiao Renshaw Wang
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 637371, Singapore
| | - S N Piramanayagam
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| |
Collapse
|
42
|
Flexible synaptic floating gate devices with dual electrical modulation based on ambipolar black phosphorus. iScience 2022; 25:103947. [PMID: 35281742 PMCID: PMC8904616 DOI: 10.1016/j.isci.2022.103947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/19/2022] [Accepted: 02/15/2022] [Indexed: 11/20/2022] Open
Abstract
Two-dimensional van der Waals materials offer various possibilities for synaptic devices, matching the requirements of intelligent and energy-efficient computation. However, very few studies on robust flexible synaptic transistors have been reported, which hold great potential for soft robotics and wearable applications. Here a floating gate synaptic device based on ambipolar black phosphorus (BP) on a flexible substrate has been demonstrated with two working modes. The three-terminal mode, where the carriers are injected into the floating gate, shows a nonvolatile memory effect, whereas the two-terminal mode shows a quasi-nonvolatile memory effect. Remarkably, the synaptic device working on the three-terminal mode shows an excellent performance in the energy-efficient computation of sub-fJ/spike with a fast gate voltage response down to ∼10 ns. Furthermore, the flexible synaptic device exhibits good endurance under 5,000 bending cycles with a strain of ∼0.63%, suggesting great potential in flexible neuromorphic applications with low energy consumption. Flexible synaptic transistors based on black phosphorus Dual electrical modulation for charge trapping in floating gate structure Nanosecond-level synaptic response and low power consumption Good endurance against mechanical bending of over thousands of times
Collapse
|
43
|
Chen F, Tang Q, Ma T, Zhu B, Wang L, He C, Luo X, Cao S, Ma L, Cheng C. Structures, properties, and challenges of emerging
2D
materials in bioelectronics and biosensors. INFOMAT 2022. [DOI: 10.1002/inf2.12299] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Fan Chen
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Qing Tang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Tian Ma
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Bihui Zhu
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Liyun Wang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Chao He
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Xianglin Luo
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| | - Sujiao Cao
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
- National Clinical Research Center for Geriatrics, West China Hospital Sichuan University Chengdu China
| | - Lang Ma
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
- National Clinical Research Center for Geriatrics, West China Hospital Sichuan University Chengdu China
- Department of Chemistry and Biochemistry Freie Universität Berlin Berlin Germany
| | - Chong Cheng
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Department of Ultrasound, West China Hospital, Med‐X Center for Materials Sichuan University Chengdu China
| |
Collapse
|
44
|
Xie H, Li Z, Cheng L, Haidry AA, Tao J, Xu Y, Xu K, Ou JZ. Recent advances in the fabrication of 2D metal oxides. iScience 2022; 25:103598. [PMID: 35005545 PMCID: PMC8717458 DOI: 10.1016/j.isci.2021.103598] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Atomically thin two-dimensional (2D) metal oxides exhibit unique optical, electrical, magnetic, and chemical properties, rendering them a bright application prospect in high-performance smart devices. Given the large variety of both layered and non-layered 2D metal oxides, the controllable synthesis is the critical prerequisite for enabling the exploration of their great potentials. In this review, recent progress in the synthesis of 2D metal oxides is summarized and categorized. Particularly, a brief overview of categories and crystal structures of 2D metal oxides is firstly introduced, followed by a critical discussion of various synthesis methods regarding the growth mechanisms, advantages, and limitations. Finally, the existing challenges are presented to provide possible future research directions regarding the synthesis of 2D metal oxides. This work can provide useful guidance on developing innovative approaches for producing both 2D layered and non-layered nanostructures and assist with the acceleration of the research of 2D metal oxides.
Collapse
Affiliation(s)
- Huaguang Xie
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Zhong Li
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Liang Cheng
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Azhar Ali Haidry
- College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Jiaqi Tao
- College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Yi Xu
- School of Materials Science and Engineering, Nanchang University, Nanchang 330031, China
| | - Kai Xu
- School of Engineering, RMIT University, Melbourne 3000, Australia
| | - Jian Zhen Ou
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
- School of Engineering, RMIT University, Melbourne 3000, Australia
| |
Collapse
|
45
|
Wang Z, Xiao W, Yang H, Zhang S, Zhang Y, Sun K, Wang T, Fu Y, Wang Q, Zhang J, Hasegawa T, He D. Resistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability. ACS APPLIED MATERIALS & INTERFACES 2022; 14:1557-1567. [PMID: 34957821 DOI: 10.1021/acsami.1c19364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ion-based memristive switching has attracted widespread attention from industries owing to its outstanding advantages in storage and neuromorphic computing. Major issues for achieving brain-inspired computation of highly functional memory in redox-based ion devices are relatively large variability in their operating parameters and limited cycling endurance. In some devices, volatile and nonvolatile operations often replace each other without changing operating conditions. To address these issues, it is important to observe directly what is happening in repeated operations. Herein, we use a planar device that enables direct capturing of microscopic behaviors in the nucleation and growth of metal whiskers under repeated switching to verify the microscopic origin of the large parameter variability. We report direct observations that reveal the physical origin for the large cycle-to-cycle and device-to-device variability in memristive switching, which was achieved using planar polymer atomic switches with a gap >1 μm. We find that the deposition location of metal atoms is closely related to the crystallinity of the ion transport layer (solid polymer electrolyte, SPE). The filament variability (shape, position, quantity, etc.) during different cycles and devices is indeed the main reason for the observed variability in the operating characteristics. The results shed unique light on the complexity of the operation of the ion device, that is, the evolution of the dielectric layer and metal filament must be considered.
Collapse
Affiliation(s)
- Zheng Wang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Wei Xiao
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Huiyong Yang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Shengjie Zhang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Yukun Zhang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Kai Sun
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Ting Wang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Yujun Fu
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Qi Wang
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Junyan Zhang
- State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences, Lanzhou 730000 China
| | - Tsuyoshi Hasegawa
- Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 Japan
| | - Deyan He
- School of Materials and Energy, Lanzhou University, Lanzhou 730000, P. R. China
| |
Collapse
|
46
|
Meng J, Wang T, Zhu H, Ji L, Bao W, Zhou P, Chen L, Sun QQ, Zhang DW. Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application. NANO LETTERS 2022; 22:81-89. [PMID: 34962129 DOI: 10.1021/acs.nanolett.1c03240] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most important perception of human beings, vision occupies more than 80% of all the received information. Inspired by biological eyes, an artificial retina based on 2D Janus MoSSe was fabricated, which could simulate functions of visual perception with electronic/ion and optical comodulation. Furthermore, inspired by human brain, sensing, memory, and neuromorphic computing functions were integrated on one device for multifunctional intelligent electronics, which was beneficial for scalability and high efficiency. Through the formation of faradic electric double layer (EDL) at the metal-oxide/electrolyte interfaces could realize synaptic weight changes. On the basis of the optoelectronic performances, light adaptation of biological eyes, preprocessing, and recognition of handwritten digits were implemented successfully. This work may provide a strategy for the future integrated sensing-memory-processing device for optoelectronic artificial retina perception application.
Collapse
Affiliation(s)
- Jialin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Tianyu Wang
- 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
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Wenzhong 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
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| |
Collapse
|
47
|
Wang B, Wang X, Wang E, Li C, Peng R, Wu Y, Xin Z, Sun Y, Guo J, Fan S, Wang C, Tang J, Liu K. Monolayer MoS 2 Synaptic Transistors for High-Temperature Neuromorphic Applications. NANO LETTERS 2021; 21:10400-10408. [PMID: 34870433 DOI: 10.1021/acs.nanolett.1c03684] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As essential units in an artificial neural network (ANN), artificial synapses have to adapt to various environments. In particular, the development of synaptic transistors that can work above 125 °C is desirable. However, it is challenging due to the failure of materials or mechanisms at high temperatures. Here, we report a synaptic transistor working at hundreds of degrees Celsius. It employs monolayer MoS2 as the channel and Na+-diffused SiO2 as the ionic gate medium. A large on/off ratio of 106 can be achieved at 350 °C, 5 orders of magnitude higher than that of a normal MoS2 transistor in the same range of gate voltage. The short-term plasticity has a synaptic transistor function as an excellent low-pass dynamic filter. Long-term potentiation/depression and spike-timing-dependent plasticity are demonstrated at 150 °C. An ANN can be simulated, with the recognition accuracy reaching 90%. Our work provides promising strategies for high-temperature neuromorphic applications.
Collapse
Affiliation(s)
- Bolun Wang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Xuewen Wang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Enze Wang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Chenyu Li
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Ruixuan Peng
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yonghuang Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Zeqin Xin
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yufei Sun
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Jing Guo
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Shoushan Fan
- Department of Physics and Tsinghua-Foxconn Nanotechnology Research Center, Tsinghua University, Beijing 100084, People's Republic of China
- State Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, People's Republic of China
| | - Chen Wang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, People's Republic of China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing 100084, People's Republic of China
| | - Kai Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China
| |
Collapse
|
48
|
Hu Y, Dai M, Feng W, Zhang X, Gao F, Zhang S, Tan B, Zhang J, Shuai Y, Fu Y, Hu P. Ultralow Power Optical Synapses Based on MoS 2 Layers by Indium-Induced Surface Charge Doping for Biomimetic Eyes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2104960. [PMID: 34655120 DOI: 10.1002/adma.202104960] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Biomimetic eyes, with their excellent imaging functions such as large fields of view and low aberrations, have shown great potentials in the fields of visual prostheses and robotics. However, high power consumption and difficulties in device integration severely restrict their rapid development. In this study, an artificial synaptic device consisting of a molybdenum disulfide (MoS2 ) film coated with an electron injection enhanced indium (In) layer is proposed to increase the channel conductivity and reduce the power consumption. This artificial synaptic device achieves an ultralow power consumption of 68.9 aJ per spike, which is several hundred times lower than those of the optical artificial synapses reported in literature. Furthermore, the multilayer and polycrystalline MoS2 film shows persistent photoconductivity performance, effectively resulting in short-term plasticity, long-term plasticity, and their transitions between each other. A 5 × 5 In/MoS2 synaptic device array is constructed into a hemispherical electronic retina, demonstrating its impressive image sensing and learning functions. This research provides a new methodology for effective control of artificial synaptic devices, which have great opportunities used in bionic retinas, robots, and visual prostheses.
Collapse
Affiliation(s)
- Yunxia Hu
- Institute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Mingjin Dai
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Wei Feng
- Department of Chemistry and Chemical Engineering, College of Science, Northeast Forestry University, Harbin, 150040, China
| | - Xin Zhang
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Feng Gao
- Institute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Shichao Zhang
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Biying Tan
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Jia Zhang
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| | - Yong Shuai
- School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - YongQing Fu
- Faculty of Engineering & Environment, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - PingAn Hu
- Institute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150001, China
| |
Collapse
|
49
|
Li L, Hu L, Liu K, Chang KC, Zhang R, Lin X, Zhang S, Huang P, Liu HJ, Kuo TP. Bifunctional homologous alkali-metal artificial synapse with regenerative ability and mechanism imitation of voltage-gated ion channels. MATERIALS HORIZONS 2021; 8:3072-3081. [PMID: 34724525 DOI: 10.1039/d1mh01012c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As a key component responsible for information processing in the brain, the development of a bionic synapse possessing digital and analog bifunctionality is vital for the hardware implementation of a neuro-system. Here, inspired by the key role of sodium and potassium in synaptic transmission, the alkali metal element lithium (Li) belonging to the same family is adopted in designing a bifunctional artificial synapse. The incorporation of Li endows the electronic devices with versatile synaptic functions. An artificial neural network based on experimental data exhibits a high performance approaching near-ideal accuracy. In addition, the regenerative ability allows synaptic functional recovery through low-frequency stimuli to be emulated, facilitating the prevention of permanent damage due to intensive neural activities and ensuring the long-term stability of the entire neural system. What is more striking for an Li-based bionic synapse is that it can not only emulate a biological synapse at a behavioral level but realize mechanism emulation based on artificial voltage-gated "ion channels". Concurrent digital and analog features lead to versatile synaptic functions in Li-doped artificial synapses, which operate in a mode similar to the human brain with its two hemispheres excelling at processing imaginative and analytical information, respectively.
Collapse
Affiliation(s)
- Lei Li
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Luodan Hu
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Kai Liu
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Kuan-Chang Chang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Rui Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Xinnan Lin
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Shengdong Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Pei Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen Graduate School, Shenzhen 518055, China.
| | - Heng-Jui Liu
- Department of Materials Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan
| | - Tzu-Peng Kuo
- Department of Physics, National Sun Yat-sen University, Kaohsiung 804, Taiwan
- Institute of Materials and Optoelectronics, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| |
Collapse
|
50
|
Nikam RD, Lee J, Choi W, Banerjee W, Kwak M, Yadav M, Hwang H. Ionic Sieving Through One-Atom-Thick 2D Material Enables Analog Nonvolatile Memory for Neuromorphic Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103543. [PMID: 34596963 DOI: 10.1002/smll.202103543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/17/2021] [Indexed: 06/13/2023]
Abstract
The first report on ion transport through atomic sieves of atomically thin 2D material is provided to solve critical limitations of electrochemical random-access memory (ECRAM) devices. Conventional ECRAMs have random and localized ion migration paths; as a result, the analog switching efficiency is inadequate to perform in-memory logic operations. Herein ion transport path scaled down to the one-atom-thick (≈0.33 nm) hexagonal boron nitride (hBN), and the ionic transport area is confined to a small pore (≈0.3 nm2 ) at the single-hexagonal ring. One-atom-thick hBN has ion-permeable pores at the center of each hexagonal ring due to weakened electron cloud and highly polarized B-N bond. The experimental evidence indicates that the activation energy barrier for H+ ion transport through single-layer hBN is ≈0.51 eV. Benefiting from the controlled ionic sieving through single-layer hBN, the ECRAMs exhibit superior nonvolatile analog switching with good memory retention and high endurance. The proposed approach enables atomically thin 2D material as an ion transport layer to regulate the switching of various ECRAM devices for artificial synaptic electronics.
Collapse
Affiliation(s)
- Revannath Dnyandeo Nikam
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Jongwon Lee
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Wooseok Choi
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Writam Banerjee
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Myonghoon Kwak
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Manoj Yadav
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Hyunsang Hwang
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| |
Collapse
|