A Digital−Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing

Author:

Chen Delu1,Zhi Xinrong1,Xia Yifan1,Li Shuhan1,Xi Benbo1,Zhao Chun2,Wang Xin1ORCID

Affiliation:

1. Henan Key Laboratory of Photovoltaic Materials Henan University Kaifeng 475004 P. R. China

2. School of Advanced Technology Xi'an Jiaotong‐Liverpool University Suzhou 215123 P. R. China

Abstract

AbstractMemristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information‐processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3‐based memristor demonstrates a high ON/OFF ratio (>103), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum‐structured piezoresistive sensor array (sensitivity of 22.4 kPa−1, durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3‐based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital−analog bimodal memristor would demonstrate potential application in human–machine interaction, prosthetics, and artificial intelligence.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Biomaterials,Biotechnology,General Materials Science,General Chemistry

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