Advanced Memory Technology

Prof. Ye Zhou is a professor in the Institute for Advanced Study, Shenzhen University. His research interests include organic/inorganic semiconductors, surface and interface physics, nanostructured materials, and nano-scale devices for technological applications, such as logic circuits, data storage, photonics and sensors.

• Memory Technology: Developments, Fundamentals, and Future Trends;Biomemristors as the Next Generation Memory Devices;Organic Resistive Memories for Neuromorphic Electronics;Low Frequency 1/f Conductance Noise in Memristors;Electrical Bistability by Creating Internal Electrical Field and Its Application in Emerging Two-terminal Electronic Memory Devices;Memory Devices Based on Low-dimensional Materials;Development, Challenges, and Future Opportunities of Spintronic Memory Devices;Dual-gate Ferroelectric Field-effect Transistors: An Emerging Computational Memory for Advanced Logic Operations;Stochastic Emerging Resistive Memories for Unconventional Computing;Indium-Gallium-Zinc Oxide (IGZO)-based ReRAM: Material Overview, Latest Development and Technology Perspective;Emerging Memristive Artificial Neurons for Energy-efficient Neuromorphic Electronic Systems;Memory, Memristive, and Neuromorphic Devices Based on Two-dimensional Transition Metal Dichalcogenides;In-sensor Computing Based on Two-terminal Optoelectronic Memristors;Memory Devices and Artificial Synapses with 2D Materials;Polymer-based Transistor-type Memory and Artificial Synapses;Amorphous Oxide Semiconductor Memristors: Brain-inspired Computation;Working Dynamics in Low-dimensional Material-based Neuromorphic Devices;Halide Perovskites for Neuromorphic Computing;Silicon Oxide-based CBRAM Memory and Neuromorphic Properties;Oxide Neuromorphic Transistors for Brain-like Computing;Sensing–Storage–Computing Integrated Devices Based on Carbon Nanomaterials;Resistive Switching-based Neuromorphic Devices for Artificial Neural Network;Silicon-based Heterostructures for Optoelectronic Synaptic Devices;Hybrid Devices for Neuromorphic Applications;Algorithmic Optimisation for Memristive Deep Learning Accelerators;Memristive Devices for Neuromorphic and Deep Learning Applications