Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi_(3.2)La_(0.8)Ti_(3)O_(12)/ITO memristor  

作　　者：Hao Chen Zhihao Shen Wen-Tao Guo Yan-Ping Jiang Wenhua Li Dan Zhang Zhenhua Tang Qi-Jun Sun Xin-Gui Tang 

机构地区：[1]School of Physics and Optoelectronic Engineering,Guangdong University of Technology,Guangzhou Higher Education Mega Center,Guangzhou,510006,China

出　　处：《Journal of Materiomics》2024年第6期1308-1316,共9页无机材料学学报（英文）

基　　金：This work was financially supported by the National Natural Science Foundation of China(Grant Nos.11574057 and 12172093);the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607).

摘　　要：Recently, memristors have garnered widespread attention as neuromorphic devices that can simulate synaptic behavior, holding promise for future commercial applications in neuromorphic computing. In this paper, we present a memristor with an Au/Bi_(3.2)La_(0.8)Ti_(3)O_(12) (BLTO)/ITO structure, demonstrating a switching ratio of nearly 103 over a duration of 104 s. It successfully simulates a range of synaptic behaviors, including long-term potentiation and depression, paired-pulse facilitation, spike-timing-dependent plasticity, spike-rate-dependent plasticity etc. Interestingly, we also employ it to simulate pain threshold, sensitization, and desensitization behaviors of pain-perceptual nociceptor (PPN). Lastly, by introducing memristor differential pairs (1T1R-1T1R), we train a neural network, effectively simplifying the learning process, reducing training time, and achieving a handwriting digit recognition accuracy of up to 97.19 %. Overall, the proposed device holds immense potential in the field of neuromorphic computing, offering possibilities for the next generation of high-performance neuromorphic computing chips.

关 键 词：Artificial synapse MEMRISTOR Resistive switching Pain-perceptual nociceptor Neuromorphic computing 

分 类 号：TQ17[化学工程—硅酸盐工业]