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Revolutionizing neuromorphic computing with memristor-based artificial neurons

Yanning Chen1, §, Guobin Zhang2, 3, 4, §, Fang Liu1, Bo Wu1, Yongfeng Deng1, Dawei Gao2, 3, 4, and Yishu Zhang2, 3, 4,

+ Author Affiliations

 Corresponding author: Dawei Gao, dawei_gao@zju.edu.cn; Yishu Zhang, zhangyishu@zju.edu.cn

DOI: 10.1088/1674-4926/24110006CSTR: 32376.14.1674-4926.24110006

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Abstract: As traditional von Neumann architectures face limitations in handling the demands of big data and complex computational tasks, neuromorphic computing has emerged as a promising alternative, inspired by the human brain's neural networks. Volatile memristors, particularly Mott and diffusive memristors, have garnered significant attention for their ability to emulate neuronal dynamics, such as spiking and firing patterns, enabling the development of reconfigurable and adaptive computing systems. Recent advancements include the implementation of leaky integrate-and-fire neurons, Hodgkin−Huxley neurons, optoelectronic neurons, and time-surface neurons, all utilizing volatile memristors to achieve efficient, low-power, and highly integrated neuromorphic systems. This paper reviews the latest progress in volatile memristor-based artificial neurons, highlighting their potential for energy-efficient computing and integration with artificial synapses. We conclude by addressing challenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic computing.

Key words: Volatile memristor, Mott memristor, diffusive memristor, artificial neurons, neuromorphic computing



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Fig. 1.  (Color online) (a) 1T1R structure based on volatile diffusive memristors. The transistor is placed in series to obtain better control of the maximum flowing current. (b) Schematic diagram of the arrangement of the silver atoms inside hafnium oxide with switching between LRS and HRS. (c) Schematic diagram of the arrangement of parallel cells based on the 1T1R cell. (d) Examples of filtered temporal current traces collected in the experiment with different frequency. (e) Histograms showing the normalization of ON device counts across various frequencies reveal a shift in the peak towards the right with frequency increase, indicating a rise in the quantity of active devices. (f) Linear mapping of log-spaced frequencies in our systems and in the cochlea. Reprinted from Ref. [26], with permission of Nature.

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Fig. 2.  (Color online) (a) Multiple types of spiking behavior such as sustained spiking, bursting, phasic spiking, and mixed-mode spiking realized by VO2-based Mott memristors. (b) VO2-based Mott memristors for artificial neurons resembling the structure of the cerebral cortex. Reprinted from Ref. [29], with permission of Nature.

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Fig. 3.  (Color online) (a) Schematic of the work-cognition tasks realized by reservoir computing systems based on optoelectronic memristive neurons. (b) Schematic of an optoelectronic memristive neuron stimulated by hybrid electrical and optical inputs. Reprinted from Ref. [9], with permission of Science.

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Fig. 4.  (Color online) (a) Diagram of the structure of the 1TFT1R volatile memristor serves as a time-surface neuron with temporal kernel function. (b) Sample image of the packaged 32 × 36 1T1R memristor array (1 kb) with the TiN/TaOx/HfO2/TiN structure wire boned in a chip holder. (c) PCB subsystem hardware for the audio recognition consists of the 1TFT volatile memristors, 1T1R memristors array, analog circuits, and communication interface. Reprinted from Ref. [69], with permission of Science.

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Fig. 5.  (Color online) Future directions of memristor-based artificial neurons for neuromorphic applications and strategies to address challenges.

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    Received: 25 November 2024 Revised: 17 December 2024 Online: Accepted Manuscript: 25 January 2025Uncorrected proof: 18 February 2025

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      Yanning Chen, Guobin Zhang, Fang Liu, Bo Wu, Yongfeng Deng, Dawei Gao, Yishu Zhang. Revolutionizing neuromorphic computing with memristor-based artificial neurons[J]. Journal of Semiconductors, 2025, In Press. doi: 10.1088/1674-4926/24110006 ****Y N Chen, G B Zhang, F Liu, B Wu, Y F Deng, D W Gao, and Y S Zhang, Revolutionizing neuromorphic computing with memristor-based artificial neurons[J]. J. Semicond., 2025, 46(6), 061301 doi: 10.1088/1674-4926/24110006
      Citation:
      Yanning Chen, Guobin Zhang, Fang Liu, Bo Wu, Yongfeng Deng, Dawei Gao, Yishu Zhang. Revolutionizing neuromorphic computing with memristor-based artificial neurons[J]. Journal of Semiconductors, 2025, In Press. doi: 10.1088/1674-4926/24110006 ****
      Y N Chen, G B Zhang, F Liu, B Wu, Y F Deng, D W Gao, and Y S Zhang, Revolutionizing neuromorphic computing with memristor-based artificial neurons[J]. J. Semicond., 2025, 46(6), 061301 doi: 10.1088/1674-4926/24110006
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      Revolutionizing neuromorphic computing with memristor-based artificial neurons

      DOI: 10.1088/1674-4926/24110006
      CSTR: 32376.14.1674-4926.24110006
      • 1.

        Beiiing Smartchip Microelectronics Technology Co., Ltd, Beijing 102200, China

      • 2.

        College of Integrated Circuits, Zhejiang University, Hangzhou 311200, China

      • 3.

        Zhejiang ICsprout Semiconductor Co., Ltd, Hangzhou 310027, China

      • 4.

        ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 310027, China

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      • Yanning Chen received the B.S. degree in computer scienee from Capital Normal University, Beijing, China, in 2002, and the M.S. degree in electronic and communication engineering from Beijing University of Posts and Telecommunications, Beijing, in 2018. She is currently pursuing the Ph.D. degree with the IC College, Zhejiang University. She is a Senior Engineer, the Director of the R & D Center. She has long been engaged in industrialchip design and generie technology research. She led the team to study the application environment of power industry chips, formulate relevant technical standards, and establish the quality assurance system of industrial chips
      • Guobin Zhang received his bachelor's degree from Harbin Institute of Technology in 2019. Now he is a master student in the College of Integrated Circuits at Zhejiang University. His research focuses on RRAM-based in-memory computing architecture and energy-efficient neuromorphic computing
      • Dawei Gao, born in March 1969, is a Researcher and Doctoral Supervisor. He graduated from Kyushu University, Japan, in 1998 with a degree in Electronic Engineering. Dr. Gao Dawei's research interests are primarily focused on integrated circuit manufacturing, logic/analog circuit processes, power devices, and power management. As the Director of the Institute of Advanced Integrated Circuit Manufacturing Technology of Zhejiang University, he is committed to exploring cutting-edge technologies in these fields and promoting the development of the Zhejiang Provincial Integrated Circuit Innovation Platform
      • Yishu Zhang is a researcher and Ph.D. supervisor at Zhejiang University, specializing in memristors, in-memory computing, neuromorphic computing, and IC process development. He received his PhD degree from Singapore University of Technology and Design in 2019. With over 30 publications and multiple patents, she leads projects funded by national and provincial grants and serves as a young editorial board member for several journals
      • Corresponding author: dawei_gao@zju.edu.cn; zhangyishu@zju.edu.cn
      • Received Date: 2024-11-25
      • Revised Date: 2024-12-17
        • Available Online: 2025-01-25

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