Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
- Authors
- Qian Chuan; Oh Seyong; Choi Yongsuk; Kim Jeong-Hoon; Sun Jia; Huang Han; Yang Junliang; Gao Yongli; Park Jin-Hong; Cho Jeong Ho
- Issue Date
- Dec-2019
- Publisher
- Elsevier BV
- Keywords
- Band engineering; Neuromorphic computing; Organic heterojunction; Pattern recognition; Solar-stimulated optoelectronic synapse
- Citation
- Nano Energy, v.66, pp 1 - 8
- Pages
- 8
- Indexed
- SCIE
SCOPUS
- Journal Title
- Nano Energy
- Volume
- 66
- Start Page
- 1
- End Page
- 8
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113741
- DOI
- 10.1016/j.nanoen.2019.104095
- ISSN
- 2211-2855
2211-3282
- Abstract
- We report an artificial optoelectronic synapse based on a copper-phthalocyanine (CuPc) and para-sexiphenyl (p-6P) heterojunction structure. This device features stable conductance states and their linear distribution in long-term potentiation (LTP) characteristic curve formed by continuous input light pulses. These superior synaptic characteristics originate from the fact that the number of photo-holes moving into the CuPc channel and photoelectrons being trapped at the p-6P/dielectric interface is constant at every light pulse. A single-layer neural network is theoretically formed with these optoelectronic synaptic devices and its feasibility is studied in terms of training/recognition tasks of the Modified National Institute of Standards and Technology digit image patterns. Owing to the excellent LTP characteristic and through the use of a unidirectional update method, its maximum recognition rate is as high as 78% despite the use of a single-layer network. This study is expected to provide a foundation for future studies on optoelectronic synaptic devices toward the implementation of complex artificial neural networks.
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