26 citations · 29 across the 7 of their papers we have counts for
7 papers
Multiscale simulations guided advances for all-optical phase-change waveguides
Hanyi Zhang, Wanting Ma, Wen Zhou +9
Photonic computing using chalcogenide phase-change materials (PCMs) is under active development for energy-efficient artificial intelligence (AI) applications. A key requirement is…
Multiscale simulations of growth-dominated SbTe phase-change material for non-volatile photonic applications
Xu-Dong Wang, Wen Zhou, Hangming Zhang +5
Chalcogenide phase-change materials (PCMs) are widely applied in electronic and photonic applications, such as non-volatile memory and neuro-inspired computing. Doped SbTe allo…
Spin glass behavior in amorphous Cr2Ge2Te6 phase-change alloy
Xiaozhe Wang, Suyang Sun, Jiang-Jing Wang +8
The layered crystal structure of Cr2Ge2Te6 shows ferromagnetic ordering at the two-dimensional limit, which holds promise for spintronic applications. However, external voltage pul…
Unlocking device-scale atomistic modelling of phase-change memory materials
Yuxing Zhou, Wei Zhang, En Ma +1
Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simula…
Designing inorganic semiconductors with cold-rolling processability
Xu-Dong Wang, Jieling Tan, Jian Ouyang +7
While metals can be readily processed and reshaped by cold rolling, most bulk inorganic semiconductors are brittle materials that tend to fracture when plastically deformed. Manufa…
In situ characterization of vacancy ordering in Ge-Sb-Te phase-change memory alloys
Ting-Ting Jiang, Xu-Dong Wang, Jiang-Jing Wang +7
Tailoring the degree of structural disorder in Ge-Sb-Te alloys is important for the development of non-volatile phase-change memory and neuro-inspired computing. Upon crystallizati…
Molecular-scale Integration of Multi-modal Sensing and Neuromorphic Computing with Organic Electrochemical Transistors
Shijie Wang, Xi Chen, Chao Zhao +11
Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint.…