Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing.

Journal: Nanoscale
PMID: 36040454

Abstract

Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.

Authors

  • Ke Zhang
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Qi Xue
    School of Information Engineering, Zhengzhou University, Zhengzhou, China.
  • Chao Zhou
    Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania.
  • Wanneng Mo
    School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Chun-Chao Chen
    State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering Shanghai Jiao Tong University, Shanghai, 200240, China. hangtao@sjtu.edu.cn.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Tao Hang
    State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering Shanghai Jiao Tong University, Shanghai, 200240, China. hangtao@sjtu.edu.cn.

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