Emerging Memory Materials and Devices

Dear Colleagues,

With the advent of the “post-Moore era”, rapid developments in the fields of the Internet of Things, big data, and artificial intelligence have challenged the traditional “von Neumann” computing architecture. Brain-inspired neuromorphic computing emulates the structure of the human brain, which is capable of parallel processing and distributed computing of complex information data while maintaining high efficiency and ultra-low power consumption. Emerging memory devices, including resistive random-access memory (RRAM), ferroelectric random access memory (FeRAM), and magnetoresistive random access memory (MRAM), are considered to be ideal hardware implementations of neuromorphic computing due to improved performance, lower power consumption, and higher data density. Over the past few decades, great efforts have been made in the development of emerging memory materials, devices, and circuits for synaptic emulation, logic operations, and neuromorphic computing. However, there are still extensive challenges to address before demonstrating the hardware implementation of neuromorphic computing. These challenges may include the operational reliability of memory devices, the biorealistic realization of synaptic functions, the algorithm and circuit of the artificial neural network, and so on. To overcome those obstacles, the study of material selection, physical mechanism, and circuit construction are still necessary. This Special Issue will cover topics that involve emerging memory materials, devices, and circuits in the context of synaptic emulation and neuromorphic computing.

Dr. Qilin Hua
Dr. Tao Zeng
Guest Editors

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• emerging memory materials and devices: RRAM, FeRAM, MRAM
• artificial synapse/neuron
• artificial sensor system
• artificial neural networks
• in-memory computing
• neuromorphic computing