大规模高均的光电子突触阵列用于人工视觉神经网络
Fanqing Zhang1,2,3, Chunyang Li1,2,3, Zhicheng Chen4,5
1State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
Microsystems & nanoengineering
|January 13, 2025
概括
研究人员为人工视觉系统开发了一种紧的28x28突触器件阵列. 这种神经形态系统整合了感知,记忆和处理,实现了人工卷积神经网络 (CNN) 96.5%的数字识别精度.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
背景情况:
- 生物启发的人工视觉系统的开发面临着大规模并行处理对记忆和识别的挑战.
- 现有的人工视觉系统需要在集成和效率方面取得重大进展.
研究的目的:
- 为人工视觉神经形态系统展示一个紧的,高度集成的突触设备阵列.
- 研究二硫化物和金纳米颗粒用于合成晶体管制造的潜力.
- 为了实现高效的视觉信息处理,包括识别和图案操纵.
主要方法:
- 使用晶圆尺度增长的单层二硫化物 (MoS2) 制造一个 28x28 阵列的高度均的浮式门突触晶体管.
- 纳入金纳米粒子 (Au NPs) 作为电子捕获层,以实现可切换电子存储和突触可塑性.
- 在784设备阵列内实现光电信号协调,用于并行处理.
主要成果:
- 通过可切换电子存储展示了各种突触可塑性行为.
- 成功实现了使用突触阵列的章和字母的写入和删除过程.
- 通过基于光学/电信号调制的人工视觉卷积神经网络 (CNN) 实现了96.5%的高数字识别精度.
结论:
- 开发的突触设备阵列为创建大规模集成的人工视觉神经形态系统提供了可行的途径.
- 传感,记忆和处理功能的集成在一个紧的形式因素是一个显著的进步.
- 使用MoS2和Au NPs为未来的神经形态硬件提供了一个有前途的材料平台.
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