可重构性
神经形态工程学
记忆电阻器
计算机科学
油藏计算
材料科学
非易失性存储器
图层(电子)
电阻式触摸屏
人工神经网络
电子工程
计算机硬件
纳米技术
人工智能
工程类
循环神经网络
电信
计算机视觉
作者
Dong Shin Kim,Phuoc Loc Truong,Cheong Beom Lee,Hyeonsu Bang,Jia Choi,S.Y. Ham,Jong Hwan Ko,Kyeounghak Kim,Daeho Lee,Hui Joon Park
出处
期刊:Small
[Wiley]
日期:2024-06-19
卷期号:20 (40): e2402961-e2402961
被引量:7
标识
DOI:10.1002/smll.202402961
摘要
Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.
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