Analog‐Tunable Nonvolatile Memristors with Self‐Compliance Behavior via a MoS _x ‐Interlayer Strategy for Neuromorphic Recognition and Zero‐Trust Image Encryption

ABSTRACT

The von Neumann memory wall severely restricts the performance of traditional computing systems, and memristors have emerged as premier candidates for neuromorphic computing by virtue of their ability to integrate storage and computation functions. However, conventional HfO _x memristors suffer from abrupt resistive switching and lack of intrinsic self‐protection, limiting their synaptic emulation performance. Herein, a novel nonvolatile HfO _x /MoS _x /HfO _x trilayer heterostructure memristor with intrinsic self‐compliance and analog conductance modulation was constructed. The optimized device exhibits stable cycling endurance, multilevel retention stability and fast response speed, and achieves linear and symmetric conductance potentiation and depression. Furthermore, the proposed memristor successfully emulates diverse biological synaptic plasticities, including EPSC, IPSC, PPF, and PPD. A constructed neural network based on this device achieves a high handwritten digit recognition accuracy of 93.84% on the MNIST dataset, with excellent noise immunity and device variation robustness. Furthermore, the memristor acts as an irreplaceable physical entropy source for zero‐trust XOR‐based image encryption, generating ciphertext images with near‐optimal information entropy reaching 7.978 bits. This work presents a feasible MoS _x interlayer modification strategy to optimize high‐performance artificial synapses on oxide memristors, enabling co‐integration of neuromorphic computing and hardware security.