A dual-ferroelectric gate-tunable memristor for physically-implemented nonlinear computing

Abstract

Nonlinear physical systems hold great promise for energy-efficient and low-hardware-cost information processing. However, their computational capabilities remain constrained by the complexity and tunability of system nonlinearity. Here we report a dual-ferroelectric gate-tunable memristor with a dipole coupling effect, achieving enlarged hysteresis, rich temporal dynamics, and nonvolatile heterosynaptic plasticity. By harnessing the dynamic nonlinearity of the dual-ferroelectric memristor, multimodal reservoir computing with an in-material fusion strategy has been achieved, which is demonstrated with a multimodal object recognition task. By exploring the static nonlinearity of the dual-ferroelectric memristor, nonlinear in-memory computing is realized with gate-tunable nonlinear functions, which successfully accelerates the Euclidean distance computation in the K-means clustering task. This work achieves strong coupling between the intrinsic physical dynamics and computational functionalities, offering new opportunities for more efficient hardware-accelerated systems.

Keywords

ferroelectric dipole coupling / gate-tunable memristor / HZO / nonlinear in-memory computing / reservoir computing / α-In₂Se₃

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2025 The Author(s). InfoMat published by UESTC and John Wiley & Sons Australia, Ltd.