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使用自我形成的分层Al和Ag纳米结构实现的神经形态设备:朝着节能和广泛的突触可塑性迈进

Rohit Attri¹, Indrajit Mondal², Bhupesh Yadav²

¹New Chemistry Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India. kulkarni@jncasr.ac.in.

Materials horizons

|November 29, 2023

概括

研究人员使用Al和Ag微纳米结构开发了一种人工突触网络 (ASN). 这种神经形态设备实现了超低能耗的脑式计算,模仿生物神经网络.

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.
电气工程电气工程

背景情况:

模仿生物神经网络是节能神经形态设备的关键.
层次结构和新兴功能对于先进的智能系统至关重要.

研究的目的:

开发一个具有层次结构的人工突触网络 (ASN).
为了实现神经形态设备的超低能耗.
了解金属元件在突触行为中的作用.

主要方法:

使用Al和Ag的微纳米结构通过干燥裂纹图案,异性质露水和自我形成制造ASN.
调查值切换特性和能源需求.
在变电流下分析电位化行为. 合规性.

主要成果:

开发的ASN具有极低能耗 (每事件约1.3 fJ) 的值切换 (Vth~1-2 V).
研究了层次配置,以确定Al和Ag组件对设备性能的贡献.
观察到与丝状结构相关的短期和长期强化行为.

结论:

人工突触网络为节能的神经形态计算提供了一个有前途的平台.
潜在的无尺度相关性有助于复制复杂的大脑行为.
这项工作推动了模仿生物神经网络的智能系统的发展.

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Neuroplasticity

Neuroplasticity

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