Issue

Vol. 18 (2026)

Self-Rectifying Memristors for Beyond-CMOS Computing: Mechanisms, Materials, and Integration Prospects

https://doi.org/10.1007/s40820-025-02035-1
Guobin Zhang
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Xuemeng Fan
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Zijian Wang
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Pengtao Li
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Zhejia Zhang
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Bin Yu
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Dawei Gao
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China
Desmond Loke
Department of Science, Mathematics, and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore
Shuai Zhong
Guangdong Institute of Intelligence Science and Technology, Hengqin 519031, People’s Republic of China
Qing Wan
Yongjiang Laboratory, Ningbo 315202, People’s Republic of China
Yishu Zhang
College of Integrated Circuits, Zhejiang University, Hangzhou 310027, People’s Republic of China

Corresponding Author: Yishu Zhang

zhangyishu@zju.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 188

Abstract

The deceleration of Moore’s law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond‑CMOS designs that integrate memory and compute. Self‑rectifying memristors (SRMs) have emerged as promising building blocks for high‑performance, low‑power systems by combining resistive switching with intrinsic diode-like behavior. Their unidirectional conduction inhibits sneak‑path currents in crossbar arrays devoid of external selectors, while nonlinear I–V characteristics, adjustable conductance states, low operating voltages, and rapid switching facilitate efficient vector–matrix operations, neuromorphic plasticity, and hardware security primitives. This review synthesizes the working mechanisms of SRMs, surveys material, and structural strategies and compares device metrics relevant to array‑scale deployment (rectification ratio, nonlinearity, endurance, retention, variability, and operating voltage). We assess SRM-enabled in-memory computing and neuromorphic applications, as well as security functions such as physical unclonable functions and reconfigurable cryptographic primitives. Integration pathways toward CMOS compatibility are analyzed, including back-end-of-line thermal budgets, uniformity, write disturb mitigation, and reliability. Finally, we outline key challenges and opportunities: materials/architecture co‑design, precision analog training, stochasticity control/exploitation, 3D stacking, and standardized benchmarking that can accelerate large‑scale SRM adoption. Through the use of specialized materials and structural optimization, SRMs are set to provide selector‑free, densely integrated, and energy‑efficient hardware for future information processing.


Highlights:
1 SRMs integrate intrinsic diode-like rectification, enabling sneak path suppression in crossbar arrays without external selectors, simplifying design, and enhancing energy efficiency for high-density in-memory computing.
2 Key metrics such as rectification ratio, nonlinearity, and CMOS compatibility are systematically reviewed, highlighting progress in 3D integration and scalable array.
3 Applications span in-memory computing, neuromorphic networks, and hardware security, with emerging potentials in in-sensor computing and self-supervised learning, positioning SRMs as pivotal beyond-CMOS building blocks.

Keywords

Self-rectifying memristor Beyond-CMOS CMOS compatibility In-memory computing Neuromorphic computing
Zhang, Guobin, Xuemeng Fan, Zijian Wang, Pengtao Li, Zhejia Zhang, Bin Yu, Dawei Gao, Desmond Loke, Shuai Zhong, Qing Wan, and Yishu Zhang. 2026. “Self-Rectifying Memristors for Beyond-CMOS Computing: Mechanisms, Materials, and Integration Prospects”. Nano-Micro Letters 18 (January):188. https://doi.org/10.1007/s40820-025-02035-1.
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