增强的突触记忆窗口和平面线性在铁电交叉点.
Yu-Rim Jeon1, Dongyoon Kim2, Chandan Biswas1
1Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
Advanced materials (Deerfield Beach, Fla.)
|December 21, 2024
概括
本研究介绍了用于神经形态计算的先进的2D二 (In2Se3) 铁电结. 这些连接实现了显著增强的内存窗口和开/关比,使高效的突触操作与低功耗.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 神经形态工程的神经形态工程
背景情况:
- 神经形态计算利用突触记忆器来实现低功耗的自适应系统.
- 当前的二维铁电突触器件在操作电压内存窗口和开/关电流比率方面存在局限性.
- 这些局限性阻碍了先进计算技术的实际应用.
研究的目的:
- 开发用于神经形态计算的高性能二维铁电突触设备.
- 为了克服现有的memristor设备在电压内存窗口和开/关比方面的局限性.
- 为了证明2D In2Se3铁电连接的潜力,以实现高效的突触操作.
主要方法:
- 使用2D In2Se3铁电连接器制造平面记忆器设备.
- 在α-In2Se3和Au电极之间集成3nmSiO2接口层.
- 设备性能的表征,包括电压内存窗口,开/关电流比率和功耗.
- 使用电脉冲模拟突触可塑性 (LTP/LTD).
- 单层感知子 (SLP) 和卷积神经网络 (CNN) 模型的芯片上训练.
主要成果:
- 实现了16V (±8V) 的记录电压内存窗口和10^8.8的开/关电流比.
- 在开启状态下,证明了10-5W的超低功耗.
- 成功模仿突触可塑性,非线性系数为LTP的1.25和LTD的-0.25.
- 在SLP和CNN模型中达到高芯片训练精度,高达90%.
- SiO2接口显著提高了设备的性能,超过了现有的二维铁电接口.
结论:
- 开发的2D In2Se3铁电接口在神经形态计算应用中表现出了卓越的性能.
- 该设备为超低功耗和高效的突触操作提供了一个有前途的解决方案.
- 增强的突触装置为下一代智能计算系统铺平了道路.
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