集成内存传感器和基于全视频光电子铁电场效应晶体管的人工视觉计算
Peng Wang1, Jie Li2, Wuhong Xue1
1Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials Science, Shanxi Normal University, Taiyuan, 030031, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 29, 2023
概括
研究人员使用范德瓦尔斯材料开发了新的2D铁电场效应晶体管 (Fe-FET),用于先进的人工智能. 这些设备展示了出色的记忆和神经形态计算能力,为高效的AI系统铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 人工智能的人工智能
背景情况:
- 对低功耗,紧的智能系统的需求推动了对传感,记忆和神经形态计算的先进材料的研究.
- 二维 (2D) 范德瓦尔斯 (vdW) 材料为设备缩放和人工智能 (AI) 的功能集成提供了独特的特性.
研究的目的:
- 设计和研究基于 2D SnS2/h-BN/CuInP2S6 (CIPS) 的全铁电场效应晶体管 (Fe-FET).
- 为了利用光诱导的铁电极化逆转来实现先进的记忆和多功能传感-记忆-计算视觉模拟.
- 模拟基本的突触可塑性,并展示神经形态计算能力.
主要方法:
- 使用SnS2/h-BN/CIPS异构结构制造2D Fe-FET.使用SnS2/h-BN/CIPS异构结构制造2D Fe-FET.
- 利用光诱导的铁电极化反转用于记忆操作.
- 模拟了突触可塑性,并进行了储库计算,用于手写数字识别.
主要成果:
- Fe-FET 实现了高开/关电流比率 (>10^5),长保留时间 (>10^4 秒) 和稳定的耐久性 (>350 周期).
- 演示了128个多层电流状态 (7位) 并模拟了各种突触可塑性特征 (PPF,STP,LTP).
- 在使用铁电光电子储计算系统的MNIST手写数字识别中实现了93.62%的准确性.
结论:
- 开发的2D Fe-FET显示出出色的记忆性能和神经形态计算功能.
- 该研究提供了一种可行的策略,用于创建多层记忆和新型神经形态视觉系统,并集成传感记忆处理.
- 这项工作推动了高效和紧的AI硬件的开发.
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