Reconfigurable Artificial Synapses with Excitatory and Inhibitory Response Enabled by an Ambipolar Oxide Thin-Film Transistor
Source abstract
Abstract
A gate-tunable synaptic device controlling dynamically reconfigurable excitatory and inhibitory synaptic responses, which can emulate the fundamental synaptic responses for developing diverse functionalities of the biological nervous system, was developed using ambipolar oxide semiconductor thin-film transistors (TFTs). Since the balanced ambipolarity is significant, a boron-incorporated SnO (SnO:B) oxide semiconductor channel was newly developed to improve the ambipolar charge transports by reducing the subgap defect density, which was reduced to less than 1017 cm–3. The ambipolar SnO:B-TFT could be fabricated with a good reproductivity at the maximum process temperature of 250 °C and exhibited good TFT performances, such as a nearly zero switching voltage, the saturation mobility of ∼1.3 cm2 V–1 s–1, s-value of ∼1.1 V decade–1, and an on/off-current ratio of ∼8 × 103 for the p-channel mode, while ∼0.14 cm2 V–1 s–1, ∼2.2 V decade–1and ∼1 × 103 for n-channel modes, respectively. The ambipolar device imitated potentiation/depression behaviors in both excitatory and inhibitory synaptic responses by using the p- and n-channel transports by tuning a gate bias. The low-power consumptions of <20 and <2 nJ per pulse for the excitatory and inhibitory operations, respectively, were also achieved. The presented device operated under an ambient atmosphere and confirmed a good operation reliability over 5000 pulses and a long-term air environmental stability. The study presents the high potential of an ambipolar oxide-TFT-based synaptic device with a good manufacturability to develop emerging neuromorphic perception and computing hardware for next-generation artificial intelligence systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Thin-Film Transistor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
Material Science and Engineering Program, University of California San Diego, La Jolla, California 92093, United States
References (48)
Show source-provided references
1. Kandel, E. R.; Schwartz, J. H.; Jessell, T. M.; Siegelbaum, S.; Hudspeth, A. J.; Mack, S. Principles of Neural Science; McGraw-Hill: New York: 2000; Vol. 4, pp 1227–1246.
2. 10.1016/0006-8993(89)91514-x10.1016/0006-8993(89)91514-x ↗
3. 10.1126/science.125464210.1126/science.1254642 ↗
4. 10.1038/nrn214810.1038/nrn2148 ↗
5. 10.1038/nature0566210.1038/nature05662 ↗
6. 10.1016/s0092-8674(01)00341-510.1016/s0092-8674(01)00341-5 ↗
7. 10.1038/nrn92010.1038/nrn920 ↗
8. 10.1109/tnn.2005.86085010.1109/tnn.2005.860850 ↗
9. Seo, J.s.; Brezzo, B.; Liu, Y.; Parker, B. D.; Esser, S. K.; Montoye, R. K.; Rajendran, B.; Tierno, J. A.; Chang, L.; Modha, D. S. In A 45nm Cmos Neuromorphic Chip with a Scalable Architecture for Learning in Networks of Spiking Neurons, 2011 IEEE Custom Integrated Circuits Conference (CICC); IEEE, 2011; pp 1–4.10.1109/CICC.2011.6055293 ↗
10. 10.3389/fnins.2015.0014110.3389/fnins.2015.00141 ↗
11. 10.1021/nl904092h10.1021/nl904092h ↗
12. 10.1021/nn202983n10.1021/nn202983n ↗
13. 10.1038/nmat305410.1038/nmat3054 ↗
14. 10.1021/nl201040y10.1021/nl201040y ↗
15. Jerry, M.; Chen, P.Y.; Zhang, J.; Sharma, P.; Ni, K.; Yu, S.; Datta, S. In Ferroelectric Fet Analog Synapse for Acceleration of Deep Neural Network Training, 2017 IEEE International Electron Devices Meeting; IEEE, 2017; pp 6.2.1–6.2. 4.10.1109/IEDM.2017.8268338 ↗
16. 10.1038/nature2574710.1038/nature25747 ↗
17. 10.1021/acs.nanolett.9b0018010.1021/acs.nanolett.9b00180 ↗
18. 10.1021/acsami.1c1832910.1021/acsami.1c18329 ↗
19. 10.1021/acsami.5b0854110.1021/acsami.5b08541 ↗
20. 10.1002/adfm.20180439710.1002/adfm.201804397 ↗
21. 10.1021/acsami.9b2292510.1021/acsami.9b22925 ↗
22. 10.1002/adma.20160431010.1002/adma.201604310 ↗
23. 10.1038/nmat485610.1038/nmat4856 ↗
24. Tang, J.; Bishop, D.; Kim, S.; Copel, M.; Gokmen, T.; Todorov, T.; Shin, S.; Lee, K.T.; Solomon, P.; Chan, K. In Ecram as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing, 2018 IEEE International Electron Devices Meeting; IEEE, 2018; pp 13.1.1–13.1.4.10.1109/IEDM.2018.8614551 ↗
25. 10.1021/acsami.7b1321510.1021/acsami.7b13215 ↗
26. 10.1021/acs.nanolett.5b0328310.1021/acs.nanolett.5b03283 ↗
27. 10.1002/aelm.20180088710.1002/aelm.201800887 ↗
28. 10.1021/acsnano.7b0303310.1021/acsnano.7b03033 ↗
29. 10.1063/1.473127110.1063/1.4731271 ↗
30. 10.1021/acsami.5b0296410.1021/acsami.5b02964 ↗
31. 10.1063/1.502287510.1063/1.5022875 ↗
32. 10.1021/acsaelm.0c0014910.1021/acsaelm.0c00149 ↗
33. 10.1021/acsami.1c1599010.1021/acsami.1c15990 ↗
34. 10.1063/1.303087310.1063/1.3030873 ↗
35. 10.1002/aelm.20200074210.1002/aelm.202000742 ↗
36. 10.1002/adma.20110141010.1002/adma.201101410 ↗
37. 10.1021/acsnano.8b0383110.1021/acsnano.8b03831 ↗
38. 10.1021/acs.nanolett.0c0354810.1021/acs.nanolett.0c03548 ↗
39. 10.1109/LED.2021.313111210.1109/LED.2021.3131112 ↗
40. 10.1063/1.9746010.1063/1.97460 ↗
41. 10.1080/1364281880821122910.1080/13642818808211229 ↗
42. Deng W. · IEEE Trans. Electron Devices · 2011 · 59 · 9410.1109/TED.2011.2172686 ↗
43. 10.1016/j.tsf.2011.07.01810.1016/j.tsf.2011.07.018 ↗
44. 10.1021/ja403586x10.1021/ja403586x ↗
45. Robie R. A. · 1995
46. 10.1038/nrn255810.1038/nrn2558 ↗
47. 10.1523/jneurosci.16-18-05661.199610.1523/jneurosci.16-18-05661.1996 ↗
48. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗