记忆电阻器
材料科学
位(键)
油藏计算
石油工程
工程物理
纳米技术
光电子学
电气工程
计算机科学
人工智能
物理
人工神经网络
工程类
计算机网络
循环神经网络
作者
Dongyeol Ju,Minsuk Koo,Sungjun Kim
出处
期刊:Nano Energy
[Elsevier BV]
日期:2024-07-04
卷期号:128: 109958-109958
被引量:1
标识
DOI:10.1016/j.nanoen.2024.109958
摘要
In this study, we employed a sputtering process to construct a memristive device within the ITO/ZrOx/TaN structure for implementing neuromorphic computing. Initially, we scanned the basic electrical properties of the ITO/ZrOx/TaN device using a DC voltage sweep on the top ITO electrode. A highly uniform gradual resistive switching phenomenon was observed over 100 cycles. The current decay in the low-resistance state was effectively controlled by the volatile memory properties. Gradual conductance changes for potentiation and depression were achieved by applying electrical pulses, enabling the establishment of multi-level conductance states. In addition, the emulation of various synaptic functions was achieved by following the learning rules of SRDP, EPSC, STDP, ADSP, Pavlovian associative learning, and PPF. Finally, 8-bit reservoir computing was demonstrated in cost-effective pattern generation and recognition, highlighting the ITO/ZrOx/TaN device's advantageous memory storage properties for synaptic characteristics.
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