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.

Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intelligence-driven materials design framework for identifying dopants that impart antiferroelectric properties to HfO₂ materials. This strategy integrates density functional theory (DFT) with machine learning (ML) techniques to swiftly screen HfO₂ materials exhibiting stable antiferroelectric properties based on the critical electric field. This approach aims to overcome the high cost and lengthy cycles associated with traditional trial-and-error and experimental methods. Among 30 undeveloped dopants, four candidate dopants demonstrating stable antiferroelectric properties were identified. Subsequent DFT analysis highlighted the Ga dopant, which displayed favorable characteristics such as a small volume change, minimal lattice deformation, and a low critical electric field after incorporation into hafnium oxide. These findings suggest the potential for stable antiferroelectric performance. Essentially, we established a correlation between the physical characteristics of hafnium oxide dopants and their antiferroelectric performance. The approach facilitates large-scale ML predictions, rendering it applicable to a broad spectrum of functional material designs.