离子液体
神经形态工程学
材料科学
电化学
信号处理
电极
信号(编程语言)
计算机科学
人工神经网络
油藏计算
表征(材料科学)
纳米技术
数字信号处理
机器学习
计算机硬件
化学
催化作用
循环神经网络
生物化学
物理化学
程序设计语言
作者
Dan Sato,Hisashi Shima,Takuma Matsuo,Minoru Yonezawa,Kentaro Kinoshita,Masakazu Kobayashi,Yasuhisa Naitoh,H. Akinaga,Shunsuke Miyamoto,Toshiki Nokami,Toshiyuki Itoh
标识
DOI:10.1021/acsami.3c08638
摘要
Device implementation of reservoir computing, which is expected to enable high-performance data processing in simple neural networks at a low computational cost, is an important technology to accelerate the use of artificial intelligence in the real-world edge computing domain. Here, we propose an ionic liquid-based physical reservoir device (IL-PRD), in which copper cations dissolved in an IL induce diverse electrochemical current responses. The origin of the electrochemical current from the IL-PRD was investigated spectroscopically in detail. After operating the device under various operating conditions, X-ray photoelectron spectroscopy of the IL-PRD revealed that electrochemical reactions involving Cu, Cu2O, Cu(OH)2, CuSx, and H2O occur at the Pt electrode/IL interface. These products are considered information transmission materials in IL-PRD similar to neurotransmitters in biological neurons. By introducing the Faradaic current components due to the electrochemical reactions of these materials into the output signal of IL-PRD, we succeeded in improving the time-series data processing performance of the nonlinear autoregressive moving average task. In addition, the information processing efficiency in machine learning to classify electrocardiogram signal waveforms was successfully improved by using the output current from IL-PRD. Optimizing the electrochemical reaction products of IL-PRD is expected to advance data processing technology in society.
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