神经形态工程学
材料科学
记忆电阻器
计算机科学
微系统
铁电性
极化(电化学)
光电子学
数据传输
计算机数据存储
人工神经网络
钙钛矿(结构)
人工智能
电子工程
纳米技术
工程类
计算机硬件
化学
电介质
物理化学
化学工程
作者
Wang Hong,Jialiang Yang,Zheng Yang,Gongjie Liu,Yusong Tang,Yiduo Shao,Xiaobing Yan
标识
DOI:10.1002/advs.202403150
摘要
Abstract Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception‐storage‐processing of light signals. Here, an optical–electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden–Popper perovskite film at room temperature. The residual polarization shows no significant decay after 10 9 ‐cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio‐synaptic long/short‐term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two‐layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical–electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.
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