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基于电解质的超薄全固态MoS2门联触晶体管与可调节的有机-无机混合膜

Jungyeop Oh¹, Seohak Park¹, Sang Hun Lee¹

¹School of Electrical Engineering, Graduate School of Semiconductor Technology, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|April 3, 2024

概括

这项研究介绍了使用二硫化物 (MoS2) 通道和可调节的混合电解质用于神经形态计算的先进的电解质门突触晶体管 (EGST). 这些设备提供高性能和低能耗,为高效的人工智能硬件铺平了道路.

科学领域:

材料科学与工程材料科学与工程
纳米技术纳米技术
神经科学和神经形态工程神经科学和神经形态工程

背景情况:

电解质开关突触晶体管 (EGST) 是由于调节导电,低功耗和多状态存储,对神经形态计算有希望.
实施基于高密度二维材料的EGST阵列在大面积通道增长和兼容固体电解质方面面临挑战.

研究的目的:

通过使用二硫化物 (MoS2) 通道和新型混合电解质聚合物,演示大规模流程兼容的全固态EGST.
利用启动化学蒸汽沉积 (iCVD) 来精确控制电解质特性和突触装置性能.

主要方法:

通过化学蒸汽沉积 (CVD) 制造MoS2通道.
合成30nm以下的有机-无机混合电解质聚合物,使用启动化学蒸汽沉积 (iCVD) 进行可调的质子导电.
描述EGST性能,包括启/关比,状态保留,耐力和能耗.

主要成果:

实现了高开/关比 (10^9) 的EGST,超过10^3的状态保留和线性导电性更新.
设备的经验证耐用性超过5×10^4周期,能耗低 (200fJ/spike).
使用制造的EGST,对MNIST手写数字识别的系统级模拟实现了93.2%的识别率.

结论:

开发的基于ICVD的混合电解质可以精确控制质子导电,从而在MoS2 EGST中实现可调节的突触性能.
展示的EGST显示出出色的突触设备特性和神经形态计算应用的实际潜力,如手写数字识别.

关键词:

人工突触是一种人造突触.深度神经网络是一个神经网络.电解质封闭晶体管电解质封闭晶体管开始了化学蒸汽沉积的过程.二硫化物是一种二硫化物.

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