Memristive Technologies for Neuromorphic Computing and Intelligent Sensing

Dear Colleagues,

Memristive devices, commonly known as memristors, have emerged as one of the most promising building blocks for next-generation neuromorphic computing and intelligent sensing systems. By mimicking the synaptic plasticity of biological neural networks, memristors offer an attractive pathway toward energy-efficient, massively parallel, and adaptive hardware that unifies perception and cognition on a single platform. As the discipline moves from standalone device demonstrations toward integrated systems that unite computing, sensing, and security capabilities, there is a clear need for a dedicated platform that captures these converging trends and fosters dialogue across the materials, device, circuit, and systems communities. The remarkable progress made over the past decade, together with the growing interest from both academia and industry, makes this an opportune moment for such a collection.

Despite this progress, several fundamental and technological challenges remain. At the device level, cycle-to-cycle and device-to-device variability, limited endurance and retention, and the trade-off between analog switching fidelity and scalability continue to demand innovative solutions. At the same time, optoelectronic memristors based on perovskite and other photoactive materials offer the appealing ability to co-locate sensing and computing within a single device, though achieving reliable and reproducible resistive switching in these emerging platforms remains an open challenge. At the circuit and architecture level, the efficient co-design of memristive arrays with peripheral electronics and the seamless integration of memristive modules into system-on-chip platforms remain critical research frontiers that warrant sustained investigation.

This Special Issue will bring together researchers from across this field of study to present the latest advances in memristive technologies. We welcome original research articles, comprehensive reviews, and forward-looking perspective papers that address topics including, but not limited to, novel resistive switching materials and mechanisms, device engineering for improved uniformity and multi-level analog switching, optoelectronic memristors and perovskite-based multifunctional devices, crossbar array design and fabrication, compute-in-memory architectures for deep learning inference and training, neuromorphic circuits and spiking neural network hardware, hardware-aware algorithms and software–hardware co-optimization, memristor-based physical unclonable functions and hardware security primitives, in-sensor computing and intelligent sensing systems, brain–computer interface applications, reliability and variability modeling, and system-level integration and benchmarking of memristive neuromorphic platforms.

By gathering these contributions, this Special Issue will offer an integrated perspective that bridges memristive device research and neuromorphic system design, complementing and extending the growing body of work in this area. We hope that this Special Issue will document emerging trends at the device–system interface and catalyze the cross-disciplinary collaborations needed to translate memristive technologies from laboratory demonstrations to practical intelligent systems.

We look forward to receiving your contributions.

Dr. Zhengwu Liu
Dr. Guan-Hua Dun
Guest Editors

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• memristor
• resistive switching
• neuromorphic computing
• compute-in-memory
• optoelectronic memristor
• artificial synapse
• crossbar array
• spiking neural network
• physical unclonable function
• in-sensor computing
• brain–computer interface
• hardware security