Highly stable self-rectifying memristor integrated arrays for simulated annealing neuromorphic computing

This work presents a high-stability self-rectifying memristor (SRM) array based on the Pt/TaO_x/Ti structure, with an in-depth investigation of the performance and potential applications of the device. The device demonstrates excellent rectification and on/off ratios, along with low-power readout, multi-state storage, and multi-level switching capabilities, highlighting its practicality and adaptability. Notably, the device exhibits outstanding fluctuation suppression and exceptional uniformity. The coefficient of variation (CV) of the rectification ratio, calculated as 0.11497 at 3 V, indicates its high stability under multiple cycles and low-voltage operation, making it well-suited for large-scale integration and operational applications. Moreover, the stability of the rectification ratio further reinforces its potential as a hardware foundation for large-scale in-memory computing systems. By combining the neuromorphic characteristics of the device with a simulated annealing algorithm and optimizing the annealing temperature function, the system emulates biological neuron behavior, enabling fast and efficient image restoration tasks. Experimental results demonstrate that this approach significantly outperforms traditional algorithms in both optimization speed and repair accuracy. The present study offers a novel perspective for the design of in-memory computing hardware and showcases promising applications in neuromorphic computing and image processing.