Memristors—Building Blocks of Future Electronics: 6G, AI, and Beyond

Dear Collegeaues,

The end of Moore's Law signals a pivotal shift in semiconductor technology, as traditional scaling confronts fundamental physical and economic barriers. To sustain the pace of this progress, researchers are exploring diverse innovations, including advanced materials, 2.5D/3D chip architectures, and alternative computing paradigms such as analog in-memory, neuromorphic, quantum, and photonic systems. Among these innovations, memristor technology emerges as a versatile bridge across approaches, enabling breakthroughs in in-memory computing, AI acceleration, and next-generation 6G communications, with the potential to redefine the electronics industry's future.

The first practical demonstration of a memristor in 2008, based on a TiO₂ thin film, was the culmination of a long-theorized concept. The drive to refine this early device quickly pushed research into new material platforms, ranging from oxides such as HfO₂ and Ta₂O₅ to two-dimensional semiconductors like MoS₂, WS₂, and hBN, as well as ferroelectrics and phase-change compounds. Each of these brought its own advances—lower switching energy, higher endurance, faster operation, or high-frequency reconfigurability—broadening the scope of what memristors can achieve.

Neuromorphic computing seeks to replicate the brain’s efficiency by moving beyond von Neumann architectures and embedding computation directly into memory. Memristors, with their ability to emulate synaptic plasticity through gradual and non-volatile resistance changes, have emerged as one of the most promising devices for this paradigm and for artificial intelligence more broadly. Organized in dense crossbar arrays, they naturally perform parallel analog computations, enabling massive acceleration of AI workloads while maintaining accuracies above 90%. Their analog resistance states encode synaptic weights directly, making them particularly well suited for deep neural networks, spiking neural networks, and other models that demand efficient training and inference at scale. By unifying storage and computation, memristors address the energy and latency bottlenecks of data movement, offering a path toward low-power, brain-inspired AI systems capable of operating efficiently from the cloud to the edge.

6G communications will require hardware that is not only faster and more energy-efficient, but also inherently reconfigurable to support diverse use cases such as terahertz communications, massive MIMO, and integrated sensing. Memristors are uniquely positioned to meet these demands: their non-volatile switching and nanoscale form factor enable tunable and adaptive RF/microwave components, including filters, phase shifters, and impedance-matching networks, that can dynamically adjust to wideband spectrum conditions. Beyond circuit reconfigurability, memristor-based analog in-memory computing can accelerate baseband and signal processing tasks such as channel equalization, beamforming, and error correction, while reducing energy and latency compared to conventional digital signal processors. By bridging device-level adaptability with system-level efficiency, memristors hold strong potential to become a key enabler of 6G architectures where communication, sensing, and AI converge.

The aim of this Special Issue is to showcase the latest advances in the fabrication, characterization, and application of memristive devices. We welcome contributions that address device design and integration, as well as modeling, simulation, and testing methodologies. Original research articles, comprehensive reviews, and short communications covering both theoretical and experimental aspects are encouraged.

Contributions could include, but are not limited to, the following topics:

• Advanced materials;
• Integration technologies;
• Scalability and memristor reliability;
• Non-volatile memories;
• Reconfigurable logic circuits;
• Neuromorphic systems;
• Memristive networks for artificial intelligence;
• RF/microwave applications;
• Sensing.

Prof. Dr. Milka Potrebić Ivaniš
Guest Editor

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• memristor
• neuromorphic computing
• artificial synapse
• nanoelectronic device
• flexible memristor