Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing

Chuan Qian, Seyong Oh, Yongsuk Choi, Jeong Hoon Kim, Jia Sun, Han Huang, Junliang Yang, Yongli Gao, Jin Hong Park, Jeong Ho Cho

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


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 photo-electrons 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.

Original languageEnglish
Article number104095
JournalNano Energy
Publication statusPublished - 2019 Dec

Bibliographical note

Funding Information:
The authors acknowledge the grants from Basic Science Research Program and Nano Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( 2017R1A2B2005790 , 2016M3A7B4910426 , and 2017R1A4A1015400 ). J.S. acknowledges support by the National Natural Science Foundation of China ( 61975241 ).

Publisher Copyright:
© 2019 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)
  • Electrical and Electronic Engineering


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