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Hu J, Li H, Zhang Y, Zhou J, Zhao Y, Xu Y, Yu B. Reconfigurable Neuromorphic Computing with 2D Material Heterostructures for Versatile Neural Information Processing. NANO LETTERS 2024. [PMID: 39038296 DOI: 10.1021/acs.nanolett.4c02658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Reconfigurable neuromorphic computing holds promise for advancing energy-efficient neural network implementation and functional versatility. Previous work has focused on emulating specific neural functions rather than an integrated approach. We propose an all two-dimensional (2D) material-based heterostructure capable of performing multiple neuromorphic operations by reconfiguring output terminals in response to stimuli. Specifically, our device can synergistically emulate the key neural elements of the synapse, neuron, and dendrite, which play important and interrelated roles in information processing. Dendrites, the branches that receive and transmit presynaptic action potentials, possess the ability to nonlinearly integrate and filter incoming signals. The proposed heterostructure allows reconfiguration between different operation modes, demonstrating its potential for diverse computing tasks. As a proof of concept, we show that the device can perform basic Boolean logic functions. This highlights its applicability to complex neural-network-based information processing problems. Our integrated neuromorphic approach may advance the development of versatile, low-power neuromorphic hardware.
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Affiliation(s)
- Jiayang Hu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Hanxi Li
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yishu Zhang
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Jiachao Zhou
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yuda Zhao
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Yang Xu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
| | - Bin Yu
- College of Integrated Circuits, Zhejiang University, Hangzhou, Zhejiang, China 311200
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, Zhejiang, China 311200
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Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X. Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors. ACS NANO 2021; 15:17319-17326. [PMID: 34541840 DOI: 10.1021/acsnano.1c04676] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.
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Affiliation(s)
- Yifei Pei
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, 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
| | - Lei Yan
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, 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
| | - Zuheng Wu
- School of Integrated Circuits, Anhui University, Hefei, Anhui 230601, P. R. China
| | - Jikai Lu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, P. R. China
| | - Jianhui Zhao
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, 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
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Qi Liu
- Frontier Institute of Chip and System Fudan University Shanghai 200433, P. R. China
| | - Xiaobing Yan
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, 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
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Lanza M, Waser R, Ielmini D, Yang JJ, Goux L, Suñe J, Kenyon AJ, Mehonic A, Spiga S, Rana V, Wiefels S, Menzel S, Valov I, Villena MA, Miranda E, Jing X, Campabadal F, Gonzalez MB, Aguirre F, Palumbo F, Zhu K, Roldan JB, Puglisi FM, Larcher L, Hou TH, Prodromakis T, Yang Y, Huang P, Wan T, Chai Y, Pey KL, Raghavan N, Dueñas S, Wang T, Xia Q, Pazos S. Standards for the Characterization of Endurance in Resistive Switching Devices. ACS NANO 2021; 15:17214-17231. [PMID: 34730935 DOI: 10.1021/acsnano.1c06980] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.
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Affiliation(s)
- Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Rainer Waser
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Peter-Grünberg-Institut (PGI-10), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen 52074, Germany
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano, 20133, Italy
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | | | - Jordi Suñe
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Anthony Joseph Kenyon
- Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Sabina Spiga
- CNR-IMM, Unit of Agrate Brianza, Via C. Olivetti 2, Agrate Brianza (MB) 20864, Italy
| | - Vikas Rana
- Peter-Grünberg-Institut (PGI-10), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Stefan Wiefels
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Stephan Menzel
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Ilia Valov
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Marco A Villena
- Applied Materials Inc., Via Ruini, Reggio Emilia 74L 42122, Italy
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Xu Jing
- School of Materials Science and Engineering, Jiangsu Key Laboratory of Advanced Metallic Materials, Southeast University, Nanjing 211189, China
| | - Francesca Campabadal
- Institut de Microelectrònica de Barcelona-Centre Nacional de Microelectrònica, Consejo Superior de Investigaciones Científicas, Bellaterra 08193, Spain
| | - Mireia B Gonzalez
- Institut de Microelectrònica de Barcelona-Centre Nacional de Microelectrònica, Consejo Superior de Investigaciones Científicas, Bellaterra 08193, Spain
| | - Fernando Aguirre
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Buenos Aires, Medrano 951(C1179AAQ), Argentina
| | - Felix Palumbo
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Buenos Aires, Medrano 951(C1179AAQ), Argentina
| | - Kaichen Zhu
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Juan Bautista Roldan
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avd. Fuentenueva s/n, Granada 18071, Spain
| | - Francesco Maria Puglisi
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena 41125, Italy
| | - Luca Larcher
- Applied Materials Inc., Via Ruini, Reggio Emilia 74L 42122, Italy
| | - Tuo-Hung Hou
- Department of Electronics Engineering and Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Themis Prodromakis
- Centre for Electronics Frontiers, University of Southampton, Southampton SO171BJ, United Kingdom
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Peng Huang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Kin Leong Pey
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372 Singapore
| | - Nagarajan Raghavan
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372 Singapore
| | - Salvador Dueñas
- Department of Electronics, University of Valladolid, Paseo de Belén 15, Valladolid E-47011, Spain
| | - Tao Wang
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University 199 Ren-Ai Road, Suzhou 215123, China
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01003-9292, United States
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Guo L, Mu B, Li MZ, Yang B, Chen RS, Ding G, Zhou K, Liu Y, Kuo CC, Han ST, Zhou Y. Stacked Two-Dimensional MXene Composites for an Energy-Efficient Memory and Digital Comparator. ACS APPLIED MATERIALS & INTERFACES 2021; 13:39595-39605. [PMID: 34378376 DOI: 10.1021/acsami.1c11014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Two-dimensional MXene has enormous potential for application in industry and academia owing to its surface hydrophilicity and excellent electrochemical properties. However, the application of MXene in optoelectronic memory and logical computing is still facing challenges. In this study, an optoelectronic resistive random access memory (RRAM) based on silver nanoparticles (Ag NPs)@MXene-TiO2 nanosheets (AMT) was prepared through a low-cost and facile hydrothermal oxidation process. The fabricated device exhibited a typical bipolar switching behavior and controllable SET voltage. Furthermore, we successfully demonstrated a 4-bit in-memory digital comparator with AMT RRAMs, which can replace five logic gates in a traditional approach. The AMT-based digital comparator may open the door for future integrated functions and applications in optoelectronic data storage and simplify the complex logic operations.
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Affiliation(s)
- Liangchao Guo
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Boyuan Mu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ming-Zheng Li
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Baidong Yang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ruo-Si Chen
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Kui Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Yanhua Liu
- Shanghai Institute of Space Power-Sources, Shanghai 200245, P. R. China
| | - Chi-Ching Kuo
- Institute of Organic and Polymeric Materials, Research and Development Center of Smart Textile Technology, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Su-Ting Han
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
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Abstract
Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.
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