Reconfigurable optoelectronic memristor for in-sensor computing applications
Inspired by human brain and visual system, optoelectronic memristors-based neuromorphic computing has attracted the interests of researchers to overcome the limitation of traditional von Neumann architecture. With advantages of highly parallel computing and massive interconnection, the optical memri...
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| Published in | Nano energy Vol. 89; p. 106291 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Inspired by human brain and visual system, optoelectronic memristors-based neuromorphic computing has attracted the interests of researchers to overcome the limitation of traditional von Neumann architecture. With advantages of highly parallel computing and massive interconnection, the optical memristors could construct light-inspired artificial neural network for neuromorphic computing tasks. Besides, nonvolatile optoelectronic memristors provide a promising path for reconfigurable logic operations, greatly promoting the development of novel in-memory computing technology. In this work, the photoelectric perception, storage and in situ computing functions were integrated in optoelectronic memristors array, which could greatly decrease the footprint of multifunctional device and improve the work efficiency of chip. The neuromorphic computing capability of the photonic memristors was verified using face images of different people with accuracy of 86.7%. Moreover, with the advantages of photoelectric cooperative modulation, the reconfigurable logic functions including “AND” and “OR” were achieved by optoelectronic memristors. The present results demonstrate the attractive bio-inspired in-sensor computing behaviors of the optoelectronic memristors, opening up potential applications of optoelectronic memristors in next-generation reconfigurable sensing-memory-computing integrated paradigms.
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•Reconfigurable optoelectronic memristors array for artificial visual system was fabricated.•The optoelectronic memristors array exhibited highly integrated in situ sensing-memory-computing capabilities.•The Boolean logic computations of “AND/OR” gates were implemented successfully.•Neuromorphic computing tasks of face recognition with accuracy of 86.7% was demonstrated. |
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| AbstractList | Inspired by human brain and visual system, optoelectronic memristors-based neuromorphic computing has attracted the interests of researchers to overcome the limitation of traditional von Neumann architecture. With advantages of highly parallel computing and massive interconnection, the optical memristors could construct light-inspired artificial neural network for neuromorphic computing tasks. Besides, nonvolatile optoelectronic memristors provide a promising path for reconfigurable logic operations, greatly promoting the development of novel in-memory computing technology. In this work, the photoelectric perception, storage and in situ computing functions were integrated in optoelectronic memristors array, which could greatly decrease the footprint of multifunctional device and improve the work efficiency of chip. The neuromorphic computing capability of the photonic memristors was verified using face images of different people with accuracy of 86.7%. Moreover, with the advantages of photoelectric cooperative modulation, the reconfigurable logic functions including “AND” and “OR” were achieved by optoelectronic memristors. The present results demonstrate the attractive bio-inspired in-sensor computing behaviors of the optoelectronic memristors, opening up potential applications of optoelectronic memristors in next-generation reconfigurable sensing-memory-computing integrated paradigms.
[Display omitted]
•Reconfigurable optoelectronic memristors array for artificial visual system was fabricated.•The optoelectronic memristors array exhibited highly integrated in situ sensing-memory-computing capabilities.•The Boolean logic computations of “AND/OR” gates were implemented successfully.•Neuromorphic computing tasks of face recognition with accuracy of 86.7% was demonstrated. |
| ArticleNumber | 106291 |
| Author | Wang, Tian-Yu Ji, Li Sun, Qing-Qing Li, Qing-Xuan Chen, Lin Zhang, David Wei Meng, Jia-Lin He, Zhen-Yu Zhu, Hao |
| Author_xml | – sequence: 1 givenname: Tian-Yu surname: Wang fullname: Wang, Tian-Yu email: tywang@fudan.edu.cn organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 2 givenname: Jia-Lin surname: Meng fullname: Meng, Jia-Lin organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 3 givenname: Qing-Xuan surname: Li fullname: Li, Qing-Xuan organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 4 givenname: Zhen-Yu surname: He fullname: He, Zhen-Yu organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 5 givenname: Hao surname: Zhu fullname: Zhu, Hao organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 6 givenname: Li surname: Ji fullname: Ji, Li organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 7 givenname: Qing-Qing surname: Sun fullname: Sun, Qing-Qing email: qqsun@fudan.edu.cn organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 8 givenname: Lin surname: Chen fullname: Chen, Lin email: linchen@fudan.edu.cn organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China – sequence: 9 givenname: David Wei surname: Zhang fullname: Zhang, David Wei organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China |
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| Keywords | In-sensor computing Boolean logic gate Optoelectronic memristor Face recognition Neuromorphic computing |
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