@article{Liu2026, 
author = {Zhaorui Liu and Caifang Gao and Jingbo Yang and Zuxin Chen and Enlong Li and Jun Li and Mengjiao Li and Jianhua Zhang},
title = {Memristor devices for next-generation computing: from performance optimization to application-specific co-design},
year = {2026},
journal = {International Journal of Extreme Manufacturing},
volume = {8},
number = {1},
keywords = {memristor, performance optimization, device design, neuromorphic computing},
url = {https://www.sciopen.com/article/10.1088/2631-7990/ae053a},
doi = {10.1088/2631-7990/ae053a},
abstract = {Memristors have emerged as a transformative technology in the realm of electronic devices, offering unique advantages such as fast switching speeds, low power consumption, and the ability to sensor-memory-compute. The applications span across non-volatile memory, neuromorphic computing, hardware security, and beyond, prompting memristors to become a versatile solution for next-generation computing and data storage systems. Despite enormous potential of memristors, the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability, device reproducibility, and array scalability. This review systematically explores recent advancements in high-performance memristor technologies, focusing on performance enhancement strategies through material engineering, structural design, pulse protocol optimization, and algorithm control. We provide an in-depth analysis of key performance metrics tailored to specific applications, including non-volatile memory, neuromorphic computing, and hardware security. Furthermore, we propose a co-design framework that integrates device-level optimizations with operational-level improvements, aiming to bridge the gap between theoretical models and practical implementations.}
}