Gate Modulation of Excitatory and Inhibitory Synaptic Plasticity in a Low-Temperature Polysilicon Thin Film Synaptic Transistor
Source abstract
Abstract
Neuromorphic computing with intelligent power-efficient data processing has become an innovative technology to overcome the performance bottleneck of traditional von Neumann-type computing architecture. As an essential element to construct a neuromorphic system, a kind of artificial synapse with high technology maturity, rich functionality, and homeostatic regulation based on simple and robust mechanism is in urgent demand. Here, we propose the dual-gate low-temperature polycrystalline silicon thin film transistor to be a prospective candidate for scalable biomimetic synapse. Fundamental bilingual homosynaptic behaviors, including excitatory postsynaptic current, inhibitory postsynaptic current, and paired pulse facilitation, have been successfully emulated based on the charge trapping mechanism under electric pulse stimulation at either top or bottom gates. The strength of the top-gated induced excitatory and inhibitory responses can be dynamically modulated by the electrical biases at the modulatory bottom gate, indicating the realization of heterosynaptic plasticity. Furthermore, the transition between excitatory and inhibitory modes can be easily controlled by the interplay of the voltage biases at top and bottom gates. These results indicate the commercial thin-film transistor technology could find its novel fundamental role in the emerging artificial intelligence era.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural Networks and Reservoir Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Wuhan National Research Center for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Department of Physics, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
Funding
Ministry of Science and Technology of the People's Republic of China · National Natural Science Foundation of China
References (48)
Show source-provided references
1. 10.1002/adma.20170472910.1002/adma.201704729 ↗
2. 10.1002/adfm.20180169010.1002/adfm.201801690 ↗
3. 10.1088/1361-6463/aac8a510.1088/1361-6463/aac8a5 ↗
4. 10.1523/JNEUROSCI.18-24-10464.199810.1523/JNEUROSCI.18-24-10464.1998 ↗
5. 10.1021/nl904092h10.1021/nl904092h ↗
6. 10.1038/nature1444110.1038/nature14441 ↗
7. 10.1038/srep0161910.1038/srep01619 ↗
8. 10.1038/nmat475610.1038/nmat4756 ↗
9. 10.1038/nmat305410.1038/nmat3054 ↗
10. 10.1038/ncomms1519910.1038/ncomms15199 ↗
11. 10.1021/nl201040y10.1021/nl201040y ↗
12. Suri, M.; Bichler, O.; Querlioz, D.; Cueto, O.; Perniola, L.; Sousa, V.; Vuillaume, D.; Gamrat, C.; DeSalvo, B. Phase Change Memory as Synapse for Ultra-Dense Neuromorphic Systems: Application to Complex Visual Pattern Extraction. In 2011 IEEE Int. Electron Devices Meeting; IEEE: Washington, DC, 2011; 4.4.1.10.1109/IEDM.2011.6131488 ↗
13. 10.1038/s41467-018-04933-y10.1038/s41467-018-04933-y ↗
14. 10.1002/adma.20170090610.1002/adma.201700906 ↗
15. 10.1002/adma.20100028210.1002/adma.201000282 ↗
16. 10.1038/ncomms415810.1038/ncomms4158 ↗
17. 10.1002/adma.20150589810.1002/adma.201505898 ↗
18. 10.1021/acsnano.6b0789410.1021/acsnano.6b07894 ↗
19. 10.1021/acsnano.8b0383110.1021/acsnano.8b03831 ↗
20. 10.1109/TED.2014.233170710.1109/TED.2014.2331707 ↗
21. Jerry, M.; Chen, P. Y.; Zhang, J.; Sharma, P.; Ni, K.; Yu, S.; Datta, S. Ferroelectric FET Analog Synapse for Acceleration of Deep Neural Network Training. In 2017 IEEE Int. Electron Devices Meeting; IEEE: San Francisco, CA, 2017; 6.2.1.10.1109/IEDM.2017.8268338 ↗
22. 10.1109/LED.2016.263953910.1109/LED.2016.2639539 ↗
23. 10.1002/adma.20170095110.1002/adma.201700951 ↗
24. 10.1002/adma.20160431010.1002/adma.201604310 ↗
25. 10.1002/adma.20180019510.1002/adma.201800195 ↗
26. 10.1038/nmat485610.1038/nmat4856 ↗
27. 10.3389/fncom.2015.0008910.3389/fncom.2015.00089 ↗
28. 10.1038/3503619110.1038/35036191 ↗
29. 10.1109/16.91822710.1109/16.918227 ↗
30. 10.1088/0268-1242/30/2/02400210.1088/0268-1242/30/2/024002 ↗
31. 10.1063/1.345799610.1063/1.3457996 ↗
32. 10.1063/1.282475810.1063/1.2824758 ↗
33. 10.1016/j.tsf.2011.09.03310.1016/j.tsf.2011.09.033 ↗
34. 10.1063/1.214605910.1063/1.2146059 ↗
35. 10.1038/ncomms265210.1038/ncomms2652 ↗
36. 10.1021/acsnano.7b0011310.1021/acsnano.7b00113 ↗
37. 10.1016/0959-4388(94)90101-510.1016/0959-4388(94)90101-5 ↗
38. 10.1038/nature0301010.1038/nature03010 ↗
39. Li, C.; Wang, Z.; Rao, M.; Belkin, D.; Song, W.; Jiang, H.; Yan, P.; Li, Y.; Lin, P.; Hu, M.; Ge, N.; Strachan, J. P.; Barnell, M.; Wu, Q.; Williams, R. S.; Yang, J. J.; Xia, Q. Long short-term memory networks in memristor crossbars. arXiv preprint arXiv:1805.11801, 2018.10.1038/s42256-018-0001-4 ↗
40. 10.1021/acsnano.7b0303310.1021/acsnano.7b03033 ↗
41. 10.1038/3507251910.1038/35072519 ↗
42. 10.1038/nature1077610.1038/nature10776 ↗
43. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
44. 10.1109/TED.2018.284951210.1109/TED.2018.2849512 ↗
45. 10.1002/adma.20150320210.1002/adma.201503202 ↗
46. 10.1002/adma.20187028710.1002/adma.201870287 ↗
47. 10.1021/acsami.8b1020310.1021/acsami.8b10203 ↗
48. 10.1002/adma.20180396110.1002/adma.201803961 ↗