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基于IGZO的第一个尖端定时触觉编码器和合增强型晶体管突触,用于高效的尖端神经网络

Dan Cai¹, Jinyong Wang², Tianchen Zhao¹

¹School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|December 8, 2025

概括

本研究介绍了用于尖端神经网络 (SNN) 的光加速硬件框架,为神经形态系统实现高效的第一尖端定时 (FST) 编码和突触学习.

科学领域:

神经形态工程的神经形态工程
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

尖端神经网络 (SNN) 需要高效的硬件实现以进行事件驱动处理.
在SNN训练中,初尖定时 (FST) 编码和高性能突触设备的紧设备级实现至关重要.
像IGZO这样的现有材料对复杂的SNN应用具有长期记忆和可塑性的局限性.

研究的目的:

提出一种新的光加速SNN硬件框架,集成传感,时间编码和突触学习.
开发和表征新材料,以改善神经元休息状态恢复和突触可塑性.
为了证明框架在现实世界应用中的有效性,如自主导航和物体检测.

主要方法:

用IGZO双TFT (PDTFT) 开发PDMS/MWCNTs片,用于毫秒级的FST触觉编码.
将GaOx/IGZO异质连接集成为光电合突触 (LECTS),以增强突触可塑性.
在LECTS中利用光和电偏差进行载波调制和屏障调整.

主要成果:

使用PDTFT设备实现了精确的毫秒级 FST触觉编码.
通过LECTS证明了超越单个刺激的突触可塑性,克服了IGZO的记忆局限性.
在自动驾驶车辆状态检测和机器人导航任务中实现了高精度 (98.4%和98.2%).
在监督SNN学习中,培训时间缩短了90.9%.

结论:

拟议的光加速SNN硬件框架为神经形态智能系统提供了一个紧而高效的解决方案.
PDTFT和LECTS的集成使得强大的传感,编码和学习能力成为可能.
这种方法为先进的低功耗神经形态计算应用铺平了道路.

关键词:

伊格佐·伊格佐 (IGZO) 是一个在这里,SNN SNN 通过合增强的合器.第一个尖刺的时间.突触突触是突触的组成部分.

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