journal article·2024-02Open access

Artificial optoelectronic synapse based on spatiotemporal irradiation to source‐sharing circuitry of synaptic phototransistors

Seungho Song, Changsoon Choi, Jongtae Ahn, Je‐Jun Lee, Jisu Jang, Byoung‐Soo Yu, Jung Pyo Hong, Yong‐Sang Ryu, Yong‐Hoon Kim, Do Kyung Hwang

InfoMat· Volume 6· Issue 2

Abstract

Abstract To overcome the intrinsic inefficiency of the von Neumann architecture, neuromorphic devices that perform analog vector–matrix multiplication have been highlighted for achieving power‐ and time‐efficient data processing. In particular, artificial synapses, of which conductance should be programmed to represent the synaptic weights of the artificial neural network, have been intensively researched to realize neuromorphic devices. Here, inspired by excitatory and inhibitory synapses, we develop an artificial optoelectronic synapse that shows both potentiation and depression characteristics triggered only by optical inputs. The design of the artificial optoelectronic synapse, in which excitatory and inhibitory synaptic phototransistors are serially connected, enables these characteristics by spatiotemporally irradiating the phototransistor channels with optical pulses. Furthermore, a negative synaptic weight can be realized without the need for electronic components such as comparators. With such attributes, the artificial optoelectronic synapse is demonstrated to classify three digits with a high recognition rate (98.3%) and perform image preprocessing via analog vector–matrix multiplication. image
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Neural dynamics and brain function

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

Affiliations

Center for Opto‐Electronic Materials and Devices, Post‐Silicon Semiconductor Institute Korea Institute of Science and Technology (KIST) Seoul Republic of Korea

School of Advanced Materials Science and Engineering Sungkyunkwan University Suwon Republic of Korea

Center for Spintronics, Post‐Silicon Semiconductor Institute Korea Institute of Science and Technology (KIST) Seoul Republic of Korea

Division of Nanoscience & Technology, KIST School University of Science and Technology (UST) Seoul Republic of Korea

School of Biomedical Engineering Korea University Seoul Republic of Korea

KU‐KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea

Funding

Korea Institute of Science and Technology · National Research Foundation of Korea · Institute for Information and Communications Technology Promotion

References (39)

Show source-provided references
1. 10.1109/85.23838910.1109/85.238389 ↗
2. Kandel ER · 2000
3. 10.1126/sciadv.abq310110.1126/sciadv.abq3101 ↗
4. HuM StrachanJP LiZY et al.Dot‐product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix‐vector multiplication. In: 2016 53rd ACM/EDAC/IEEE Design Automation Conf. (DAC); 2016:1‐6.10.1145/2897937.2898010 ↗
5. 10.1109/4.5030510.1109/4.50305 ↗
6. 10.1002/adma.20220664810.1002/adma.202206648 ↗
7. 10.1002/aisy.20200013410.1002/aisy.202000134 ↗
8. 10.1038/nmat305410.1038/nmat3054 ↗
9. 10.1002/adma.20210501710.1002/adma.202105017 ↗
10. 10.1002/aisy.20220019610.1002/aisy.202200196 ↗
11. 10.1021/acsphotonics.2c0158310.1021/acsphotonics.2c01583 ↗
12. 10.1126/sciadv.abg145510.1126/sciadv.abg1455 ↗
13. 10.1038/s41565-019-0501-310.1038/s41565-019-0501-3 ↗
14. 10.1038/s41928-022-00840-910.1038/s41928-022-00840-9 ↗
15. 10.1002/adma.20210662510.1002/adma.202106625 ↗
16. 10.1002/aisy.20200009910.1002/aisy.202000099 ↗
17. 10.1002/adma.20210243510.1002/adma.202102435 ↗
18. 10.1002/advs.20220212310.1002/advs.202202123 ↗
19. 10.1016/j.nanoen.2021.10629110.1016/j.nanoen.2021.106291 ↗
20. 10.1002/adma.20200243110.1002/adma.202002431 ↗
21. 10.1038/s41467-022-32790-310.1038/s41467-022-32790-3 ↗
22. 10.1038/s41467-018-07572-510.1038/s41467-018-07572-5 ↗
23. 10.1002/adma.20200420710.1002/adma.202004207 ↗
24. 10.1002/adfm.20200159810.1002/adfm.202001598 ↗
25. 10.1002/adom.20220040910.1002/adom.202200409 ↗
26. 10.1002/aelm.20210140210.1002/aelm.202101402 ↗
27. 10.1038/ncomms307210.1038/ncomms3072 ↗
28. 10.1038/s41565-021-00943-y10.1038/s41565-021-00943-y ↗
29. 10.1038/s41467-021-25455-010.1038/s41467-021-25455-0 ↗
30. 10.1007/BF0023574810.1007/BF00235748 ↗
31. 10.1113/jphysiol.1952.sp00476410.1113/jphysiol.1952.sp004764 ↗
32. 10.1063/1.495504210.1063/1.4955042 ↗
33. 10.1016/j.nanoen.2018.08.01810.1016/j.nanoen.2018.08.018 ↗
34. 10.1002/advs.20210463210.1002/advs.202104632 ↗
35. ChenPY PengXC YuSM.NeuroSim plus: an integrated device‐to‐algorithm framework for benchmarking synaptic devices and array architectures. In: 2017 IEEE International Electron Devices Meeting (IEDM); 2017:6.1.1‐6.1.4.10.1109/IEDM.2017.8268337 ↗
36. 10.1038/s42256-018-0001-410.1038/s42256-018-0001-4 ↗
37. 10.1371/journal.pcbi.100303710.1371/journal.pcbi.1003037 ↗
38. The open CV library
39. Scikit‐learn: machine learning in python