电压
铁电性
材料科学
电压源
光电子学
油藏计算
电气工程
计算机科学
工程类
人工神经网络
机器学习
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:10.35848/1347-4065/adb295
摘要
Abstract We investigate FeFET reservoir computing (RC) utilizing a voltage readout scheme instead of the conventional current readout scheme to reduce the power consumption of an RC system and expand the range of applications. The voltage-output RC operates with time-series Vout data of a source follower of a FeFET (FeSF), to effectively utilize temporal memory as well as nonlinearity originating from ferroelectric polarization dynamics. We show that this RC system can solve several nonlinear tasks including time-series data prediction. Furthermore, to achieve higher computational performance in voltage-output FeSF RC, an FET is inserted between the source of the FeFET and the ground, which can operate as a current source to mitigate the slow pull-down operation of voltage-output FeSF RC. We also propose an RC system utilizing voltage responses from complementary working FeSFs with original and inverted gate input patterns to demonstrate high RC performance.
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