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

## TL;DR

Self-rectifying memristors (SRMs) offer energy-efficient computing by eliminating the need for external selectors in crossbar arrays, making them promising for next-generation in-memory and neuromorphic systems.

## Contribution

This paper systematically reviews SRM mechanisms, materials, and integration strategies, emphasizing their potential for beyond-CMOS computing and addressing key challenges for large-scale adoption.

## Key findings

- SRMs enable selector-free crossbar arrays with improved energy efficiency and simplified design.
- Advances in 3D integration and CMOS compatibility suggest scalable deployment for in-memory computing and neuromorphic applications.
- SRMs support hardware security functions like physical unclonable functions and reconfigurable cryptography.

## Abstract

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.Key metrics such as rectification ratio, nonlinearity, and CMOS compatibility are systematically reviewed, highlighting progress in 3D integration and scalable array.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.

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.

Key metrics such as rectification ratio, nonlinearity, and CMOS compatibility are systematically reviewed, highlighting progress in 3D integration and scalable array.

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.

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.