Substrate-Voltage-Controlled Temporal Nonlinearity in Ferroelectric FET-based Reservoir Computing

TL;DR

This paper presents a ferroelectric FET-based reservoir computing system that enhances temporal and spatial nonlinearity by utilizing both gate and substrate inputs, improving processing of complex time-series data.

Contribution

It introduces substrate input control in FeFETs to augment nonlinearity and memory, enabling more diverse internal states for reservoir computing.

Findings

Enhanced short-term memory and nonlinearity in FeFET reservoir system

Improved feature extraction from complex time-series data

Demonstrated energy-efficient and flexible reservoir computing platform

Abstract

Physical reservoir computing exploits inherent nonlinearity and short-term memory of physical dynamics to achieve efficient processing of time-series data with extremely-low training cost. In this study, we demonstrate a ferroelectric field-effect transistor (FeFET)-based reservoir computing system with augmented temporal and spatial nonlinearity by utilizing both gate and substrate terminals as inputs. The ferroelectric polarization state in the next time step can additionally be controlled by modifying the electric field distribution in the gate stack of FeFET through a substrate input, enabling more diverse internal states compared with the case where inputs are applied only to the gate. To introduce a nonlinearity in the time domain, we introduce a delay between a gate input and a substrate input, which facilitates efficient nonlinear mixing between the current and past inputs. As a result, both the short-term memory and nonlinearity of the FeFET reservoir computing system are enhanced with an improved capability of feature extraction of complex input time-series. These findings demonstrate that introducing substrate input provides an additional degree of freedom for controlling ferroelectric polarization dynamics, enabling a flexible, energy-efficient, and highly integrable FeFET-based reservoir computing platform suitable for diverse time-series processing applications.