Fully Ferroelectric-FETs Reservoir Computing Network for Temporal and Random Signal Processing
Mingfeng Tang, Junyao Mei, Xuepeng Zhan, Chengcheng Wang, Junshuai Chai, Hao Xu, Xiaolei Wang, Jixuan Wu, Jiezhi Chen
Abstract
Reservoir computing (RC), a derivation of recurrent neural networks (RNNs), is an energy-efficient computational framework suitable for temporal signal processing. Owing to the short-term and long-term memory capability, the ferroelectric field-effect transistor (FeFET) is regarded as a promising hardware component for implementing RC networks. This article aims to optimize the fully FeFETs RC network by evaluating the recognition accuracy in various classification tasks, which includes the operating voltage sequence, device numbers as well as connection methods. The physical random telegraph noise (RTN), working as an ideal temporal and random signal, is investigated and extended by using the optimized fully FeFET RC network, resulting in a rapid time constant extraction method. Our findings may provide the broad potential for hardware security and cyber security based on the fully FeFET RC network.