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Title: Dynamic Ferroelectric Transistor-based Reservoir Computing for Spatiotemporal Information Processing
Authors: Ngoc Thanh Duong 
Yu-Chieh Chien
Heng Xiang 
Sifan Li 
Haofei Zheng
Yufei Shi
Kah Wee Ang 
Keywords: artificial neural networks
fading effect
ferroelectric field-effect transistors
in-memory computing reservoir computing
spatiotemporal information processing
Issue Date: 19-Feb-2023
Citation: Ngoc Thanh Duong, Yu-Chieh Chien, Heng Xiang, Sifan Li, Haofei Zheng, Yufei Shi, Kah Wee Ang (2023-02-19). Dynamic Ferroelectric Transistor-based Reservoir Computing for Spatiotemporal Information Processing. Advanced Intelligent Systems. ScholarBank@NUS Repository.
Rights: CC0 1.0 Universal
Abstract: Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two-terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field-effect transistor (Fe-FET) based on α-In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α-In2Se3 is sufficiently investigated by polarization dynamic model. The proposed Fe-FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time-series real-life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro-RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient (R 2) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10−3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system.
Source Title: Advanced Intelligent Systems
ISSN: 2640-4567
Rights: CC0 1.0 Universal
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