Knowledge Management System of Hefei Institute of Physical Science,CAS
Phase-Transition-Induced VO2 Thin Film IR Photodetector and Threshold Switching Selector for Optical Neural Network Applications | |
Zhou, Xi1,2,3; Zhao, Liang4,5; Zhen, Weili6; Lin, Yinyue1,2,3; Wang, Chunlin1,2,3; Pan, Tianyu1,2,3; Li, Le1,2,3; Du, Guanlin1,2,3; Lu, Linfeng1,2,3; Cao, Xun7; Li, Dongdong1,2,3 | |
2021-03-30 | |
发表期刊 | ADVANCED ELECTRONIC MATERIALS |
ISSN | 2199-160X |
通讯作者 | Zhao, Liang(leonzhao@reliancememory.com) ; Li, Dongdong(lidd@sari.ac.cn) |
摘要 | As the architecture of choice for future artificial-intelligent systems, the ideas of in-memory- and in-sensor-computing paradigms based on non-von-Neumann architecture possess broad application prospects such as neuromorphic and sensor-memory-processor fusion systems. At the same time, these promising applications put diversified and strict requirements on the device performances, such as fast response to external signals, robust data security, and 3D integration potential. In this work, Au@VO2 IR photodetectors and Ti/Au/VO2/Ti/Au threshold switching selectors are constructed, where the VO2 thin films are realized by magnetron sputtering and water-vapor assisted post-annealing. Fast IR response is achieved in Au@VO2 photodetectors through a surface plasmon resonance-assisted metal-insulator transition. Furthermore, electroforming-free, tunable threshold voltage, steep switching slope, and selectivity of more than two orders of magnitude are observed in Ti/Au/VO2/Ti/Au threshold switching selector. Combining the functionalities of photodetection and selector, a VO2-based optical convolution engine demonstrates accurate and secure image-processing capability. These VO2-based devices are demonstrated as promising candidates for novel non-volatile memory, neuromorphic computing and sensor-memory-processor fusion applications. |
关键词 | neuromorphic computing optical neural networks photodetectors threshold switching vanadium dioxide |
DOI | 10.1002/aelm.202001254 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61622407] ; Natural Science Foundation of Shanghai[19ZR1479100] ; Shanxi Science and Technology Department[20201101012] |
项目资助者 | National Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Shanxi Science and Technology Department |
WOS研究方向 | Science & Technology - Other Topics ; Materials Science ; Physics |
WOS类目 | Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied |
WOS记录号 | WOS:000634682300001 |
出版者 | WILEY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.hfcas.ac.cn:8080/handle/334002/121356 |
专题 | 中国科学院合肥物质科学研究院 |
通讯作者 | Zhao, Liang; Li, Dongdong |
作者单位 | 1.Chinese Acad Sci, Shanghai Adv Res Inst, CAS Key Lab Low Carbon Convers Sci & Engn, 99 Haike Rd,Zhangjiang Hitech Pk, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Adv Res Inst, Interdisciplinary Res, 99 Haike Rd,Zhangjiang Hitech Pk, Shanghai 201210, Peoples R China 3.Univ Chinese Acad Sci, Sch Microelect, 19 Yuquan Rd, Beijing 100049, Peoples R China 4.Hefei Reliance Memory Ltd, Bldg F4-11F,Innovat Ind Pk Phase 2, Hefei 230088, Peoples R China 5.Zhejiang Univ, Coll Informat Sci & Elect Engn, 38 Zheda Rd, Hangzhou 310007, Peoples R China 6.Chinese Acad Sci, High Magnet Field Lab, Hefei 230031, Peoples R China 7.Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine, Shanghai 200050, Peoples R China |
推荐引用方式 GB/T 7714 | Zhou, Xi,Zhao, Liang,Zhen, Weili,et al. Phase-Transition-Induced VO2 Thin Film IR Photodetector and Threshold Switching Selector for Optical Neural Network Applications[J]. ADVANCED ELECTRONIC MATERIALS,2021. |
APA | Zhou, Xi.,Zhao, Liang.,Zhen, Weili.,Lin, Yinyue.,Wang, Chunlin.,...&Li, Dongdong.(2021).Phase-Transition-Induced VO2 Thin Film IR Photodetector and Threshold Switching Selector for Optical Neural Network Applications.ADVANCED ELECTRONIC MATERIALS. |
MLA | Zhou, Xi,et al."Phase-Transition-Induced VO2 Thin Film IR Photodetector and Threshold Switching Selector for Optical Neural Network Applications".ADVANCED ELECTRONIC MATERIALS (2021). |
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