CMOS芯片
铁电性
模式(计算机接口)
电压
材料科学
油藏计算
光电子学
电气工程
电子工程
计算机科学
工程类
人工智能
操作系统
人工神经网络
电介质
循环神经网络
作者
Rikuo Suzuki,Kasidit Toprasertpong,Ryosho Nakane,Eishin Nako,Mitsuru Takenaka,Shinichi Takagi
标识
DOI:10.35848/1882-0786/ade199
摘要
Abstract We propose reservoir computing (RC) utilizing a CMOS inverter composed of ferroelectric FETs (FeCMOS) to enhance energy efficiency and computational capability. We confirmed that the output voltage of an FeCMOS exhibits hysteresis characteristics originating from ferroelectric polarization dynamics and the FeCMOS RC has the capability to solve fundamental nonlinear problems. Furthermore, we introduced a technique to enhance the computational capability of FeCMOS RC by adjusting the center of the operating voltage according to the threshold voltage. This approach facilitates transient dynamics with a wider range of intermediate output voltage and enhances the performance of the RC system. We also demonstrate that FeCMOS RC can solve nonlinear time-series prediction tasks with higher energy efficiency than conventional FeFET RC systems.
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