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.

With the continuous advancement of advanced processes and technologies, in order to ensure the accuracy of data during high-speed transmission, equalizers need to provide higher compensation and lower power consumption to achieve efficient communication. A high-gain and low-power adaptive CTLE (continuous time linear equalizer) was designed on the basis of the 12 nm CMOS (complementary metal-oxide-semiconductor) process, which adopted a two-stage cascade structure to compensate for channel attenuation and improve the quality of the received signal. In addition, the adaptive module used the SS-LMS (sign-sign least mean square) algorithm to accelerate the convergence speed of the tap coefficients. Simulation results show that when the transmission rate is 16 Gbit/s, the equalizer can compensate for a half-bit rate channel attenuation of -15.53 dB, and the equalizer coefficients converge within 16×10⁴ unit interval data. Moreover, after convergence, the received error rate is lower than 10^-12.

Self-rectifying memristor (SRM) arrays hold tremendous potential in high-density data storage and energy-efficient neuromorphic computing. However, SRM arrays are mostly developed on rigid substrates and lack mechanical flexibility, limiting their applications in intelligent electronic skin, wearable technologies, etc. Here, we present a high-performance SRM array based on Pt/HfO₂/Ta₂O_5−x/Ti heterojunctions, which can be fabricated on a flexible polyimides (PI) substrate and demonstrates exceptional memristive performance under bending conditions (bending radius (R) = 1 cm, rectifying ratio > 10⁴, retention time > 10⁴ s and endurance > 10⁵ cycles). We demonstrate a 16 × 16 flexible memristor array offering noise filtering and data storage capabilities, which can be used to accurately process and store the signals transmitted by a pressure sensor array. This research represents an important advancement towards the realization of next-generation high-performance flexible electronics.

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.