Neuromorphic Memory and Computing-in-Memory Architectures: From Devices to Systems

Dear Colleagues,

Neuromorphic memory and computing-in-memory architectures are poised to reshape the future of intelligent information processing. By emulating the parallel processing and adaptive learning capabilities of biological neural networks, neuromorphic memory devices deliver energy-efficient data storage and dynamic synaptic functionality. In parallel, computing-in-memory architectures overcome the von Neumann bottleneck by integrating logic and memory within the same physical units, offering substantial advances in latency reduction, scalability, and power efficiency.

Rapid progress in emerging memory technologies—including filament-free and non-volatile defect-engineered memristors, optoelectronic and photoresponsive memories, and photonic–electronic hybrid synaptic devices—is accelerating the development of highly dense and reliable neuromorphic hardware. At the system and algorithm levels, innovations such as high-linearity analog matrix-vector multiplication, error-resilient crossbar architectures, neuromorphic accelerators, and full-stack algorithm–hardware co-optimization are driving real-world deployment for edge intelligence, autonomous navigation, human–machine interaction, and cognitive sensing.

This Special Issue provides a dedicated forum for breakthroughs spanning materials, devices, architectures, and system-level integration for neuromorphic memory and computing-in-memory technologies. We invite submissions that explore new device physics, scalable fabrication strategies, low-power and high-precision neuromorphic computing algorithms, heterogeneous integration, 3D stacking, and cross-domain intelligence. Interdisciplinary studies bridging materials science, semiconductor engineering, and artificial intelligence are strongly encouraged.

Dr. Jinyong Wang
Prof. Dr. Jing Liu
Guest Editors

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