Robust Sb 2 Se 3 /Hafnium Zirconium Oxide Synaptic Memristors with Dynamic Learning Rate Strategy for Generalizable Neuromorphic Networks

ABSTRACT Generalization across diverse tasks is essential for neuromorphic computing hardware based on memristors. Achieving on‐memristor synaptic modulation from short‐term plasticity (STP) to dynamically tuned long‐term potentiation/depression (LTP/LTD) provides a promising path for adaptive learning, addressing the energy‐function trade‐off in neuromorphic learning systems. Here, we present a solution‐processed Ag/Sb 2 Se 3 /HZO/Si memristor fabricated, exhibiting STP, LTP/LTD, and linear conductance modulation with tunable slope. Integrated into an energy‐efficient reservoir computing (RC) system with adaptive pulse periods, the network achieves 90.33% accuracy on MNIST with enhanced energy efficiency and faster convergence. To enhance learning adaptability, we introduce a memristor‐inspired dynamic learning rate scheduling (DLRS) strategy that leverages conductance tunability for stage‐wise training adaptation. This approach enables rapid convergence and strong generalization, achieving 83.69% test accuracy on Fashion‐MNIST, and a prediction error of 1 × 10 −5 on LSTM‐based time‐series forecasting. The DLRS strategy also improves performance for adaptive neuromorphic learning across diverse driving scenarios in reinforcement learning (RL) tasks. Therefore, this work establishes a critical device‐algorithm interface, showcasing the potential of multi‐modal memristors for reconfigurable, energy‐efficient, and self‐adaptive neuromorphic hardware.