神经形态工程学
MNIST数据库
冯·诺依曼建筑
晶体管
计算机科学
纳米线
计算机数据存储
材料科学
纳米技术
电气工程
光电子学
电子工程
深度学习
人工神经网络
计算机硬件
工程类
人工智能
电压
操作系统
作者
Xiaoliang Hu,Rui Hao,Lili Luo,Youwei Zhang,Yingtao Li,Zemin Zhang
标识
DOI:10.1002/lpor.202400319
摘要
Abstract Confronting the limitations of traditional von Neumann architectures—chiefly their inability to process unstructured data efficiently, this work proposes a new in‐memory sensing and computing paradigm exemplified by introducing a pioneering tin oxide nanowire‐based photoelectric synaptic transistor (PST) with a floating gate replicating biological synapses' functionality. Capitalizing on the nanowire structure's high surface area to volume ratio, the PST overcomes the challenges of photo response in metal oxide semiconductors. Integrating metal oxides' Photoelectric‐Phenomenon‐Coupling effect with the charge storage capacity of field‐effect transistors enables effective optical detection and weight storage. The PST performs exceptional optoelectrical and synaptic properties, including capabilities for long‐term memory, short‐term memory, paired‐pulse facilitate, and synaptic plasticity. Furthermore, by incorporating a learning mechanism, the PST achieves an impressive 94.65% accuracy in recognizing patterns in the Modified National Institute of Standards and Technology's(MNIST) handwritten digit dataset within 50 epochs. This research indicates a significant step toward intelligent computing systems closely mimicking the human brain's computational prowess.
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