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一个基于InSe/电荷捕获层/h-BN异质连接的人工突触装置,通过氧气等离子处理可控制的电荷捕获

Qinghui Wang^1,2, Jiayong Wang³, Manjun Lu⁴

¹School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650500, China.

|December 31, 2025

概括

这项研究介绍了一种新型的人工突触装置,使用化 (InSe) 和充电捕获层来增强神经形态计算. 该设备展示了突触行为的稳定模拟,为先进的AI硬件铺平了道路.

科学领域:

材料科学材料科学材料科学
计算机工程计算机工程
纳米技术纳米技术

背景情况:

神经形态计算试图超越传统·诺伊曼架构的局限性.
二维材料对人工突触有希望,但往往缺乏稳定性.
印化 (InSe) 显示出神经形态装置的潜力,但研究仍在芽.

研究的目的:

开发一种稳定高效的人工突触装置,使用一种新的InSe/Charge Trapping Layer (CTL) /h-BN异质连接.
研究氧气等离子处理对h-BN产生可控制的电荷捕获层的影响.
模拟基本的突触行为,并为神经形态应用建立结构-活动关系.

主要方法:

一个InSe/CTL/h-BN异质连接装置的制造.
氧气等离子处理h-BN以创建一个电荷捕获层.
突触行为的表征,如配对脉冲促进和长期的增强/抑制.

主要成果:

该设备成功模拟了关键的突触行为,具有很高的可重现性和稳定性.
证明了出色的线性,对称性和短期和长期可塑性之间的动态转换.
实现了高动态范围比率 (7.12) 和强大的多层导电性调,经过64个周期的验证.

结论:

基于InSe的突触设备为高性能神经形态计算提供了一个有前途的平台.
了解接口电荷存储机制对于设备优化至关重要.
这项研究对大脑启发的计算和人工智能硬件开发具有重大潜力.

关键词:

一个人的内心.人工突触装置是一种人工突触装置.充电捕获层是一个充电捕获层.异质连接异质连接神经形态计算是一种神经形态计算.

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