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Dynamic Ferroelectric Transistor-Based Reservoir Computing for Spatiotemporal Information Processing
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DOI：10.1002/aisy.202300009.png)
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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 alpha-In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in alpha-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 x 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.
Keyword:
artificial neural networks
fading effect
ferroelectric field-effect transistors
in-memory computing reservoir computing
spatiotemporal information processing
期刊
IF:
6.1
论文数:
2.0K
被引数:
8.4K
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