journal article·2020-03-04

Rational Band Engineering of an Organic Double Heterojunction for Artificial Synaptic Devices with Enhanced State Retention and Linear Update of Synaptic Weight

Chuan Qian, Seyong Oh, Yongsuk Choi, Seunghwan Seo, Jia Sun, Jin-Hong Park, Jeong Ho Cho

Publisher page

Abstract

Herein, we propose an organic double heterojunction to enable a nonvolatile step modulation of the conductance of an artificial synapse; the double heterojunction is composed of N,N′-dioctyl-3,4,9,10-perylene tetracarboxylic diimide (PTCDI-C8), copper phthalocyanine (CuPc), and para-sexiphenyl (p-6P). The carrier confinement in the CuPc region present in the double-heterojunction structure enabled the nonvolatile modulation of the postsynaptic current. The proposed organic synapse exhibited an excellent conductance change, characteristic with a nonlinearity (NL) value below 0.01 in the long-term potentiation (LTP) region. Furthermore, the NL value for long-term depression (LTD) could be reduced effectively from 45 to 3.5 by a pulse modulation technique. A simple artificial neural network (ANN) was theoretically designed using the LTP/LTD characteristic curves of such organic synapses, and then, learning and recognition tasks were performed using Modified National Institute of Standards and Technology digit images. A four-amplitude weight update method enabled considerable enhancement of the recognition rate from 53 to 70%. Although the designed ANN was based on a single-layer perceptron model, a high maximum accuracy of 75% was achieved. These newly studied techniques for synaptic devices are expected to open up new possibilities for the realization of artificial synapses based on organic double heterojunctions.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 120-749, Republic of Korea

Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China

SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea

Funding

National Research Foundation of Korea

References (31)

Show source-provided references
1. 10.1038/nature1444110.1038/nature14441 ↗
2. 10.1109/ted.2011.214779110.1109/ted.2011.2147791 ↗
3. 10.1038/nmat485610.1038/nmat4856 ↗
4. 10.1021/acsami.5b0854110.1021/acsami.5b08541 ↗
5. 10.1002/smll.20190069510.1002/smll.201900695 ↗
6. 10.1126/science.aao009810.1126/science.aao0098 ↗
7. 10.1126/science.aaw558110.1126/science.aaw5581 ↗
8. 10.1038/s41598-017-04641-510.1038/s41598-017-04641-5 ↗
9. 10.1088/1361-6463/aacd9910.1088/1361-6463/aacd99 ↗
10. 10.1002/aelm.20170006310.1002/aelm.201700063 ↗
11. 10.1038/am.2017.13310.1038/am.2017.133 ↗
12. 10.1063/1.493855310.1063/1.4938553 ↗
13. 10.1002/adfm.20180559910.1002/adfm.201805599 ↗
14. 10.1038/srep3335510.1038/srep33355 ↗
15. 10.1002/adma.20180622710.1002/adma.201806227 ↗
16. 10.1002/adfm.20180085410.1002/adfm.201800854 ↗
17. 10.1016/j.nanoen.2019.10409510.1016/j.nanoen.2019.104095 ↗
18. 10.1002/adma.20090355910.1002/adma.200903559 ↗
19. 10.1038/nature0553310.1038/nature05533 ↗
20. 10.1126/science.117996310.1126/science.1179963 ↗
21. 10.1002/marc.20090007510.1002/marc.200900075 ↗
22. 10.1021/acsphotonics.7b0089810.1021/acsphotonics.7b00898 ↗
23. 10.1038/asiamat.2010.4410.1038/asiamat.2010.44 ↗
24. 10.1002/aelm.20150029810.1002/aelm.201500298 ↗
25. 10.1021/acsami.6b0886610.1021/acsami.6b08866 ↗
26. 10.1002/advs.20170000710.1002/advs.201700007 ↗
27. 10.1063/1.494757610.1063/1.4947576 ↗
28. 10.1021/acs.jpcc.5b0372710.1021/acs.jpcc.5b03727 ↗
29. 10.1146/annurev.neuro.23.1.64910.1146/annurev.neuro.23.1.649 ↗
30. Chen, P.; Peng, X.; Yu, S. NeuroSim+: An Integrated Device-to-Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures. 2017 IEEE International Electron Devices Meeting (IEDM), 2017; 6.1.1–6.1.4.10.1109/IEDM.2017.8268337 ↗
31. 10.1038/ncomms415810.1038/ncomms4158 ↗