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. image