Visible-Light-Stimulated Optoelectronic Neuromorphic Transistor Based on Indium–Gallium–Zinc Oxide via Bi2Te3 Light Absorption Layer
Source abstract
Abstract
To emulate a visual perception system, a bismuth telluride (Bi 2 Te 3 )/indium–gallium–zinc oxide (IGZO) heterostructure is introduced for optoelectronic neuromorphic transistors (ONTs). Amorphous IGZO is applied as a channel layer to exhibit low off-current, high mobility, and persistent photoconductivity, enabling light-stimulated neuromorphic characteristics. The atomic ratio of In/Ga/Zn was 9.4:9.8:5.2. For a light absorption layer, Bi 2 Te 3 is applied due to a small bandgap, high photoresponse, and carrier concentration. However, conventional optoelectronic devices using Bi 2 Te 3 exhibit insufficient performance owing to their excessive conductivity. To resolve the constraint, the oxidation of Bi 2 Te 3 is performed to suppress its electrical conductivity. Finally, the IGZO ONT with a Bi 2 Te 3 layer exhibits optoelectronic characteristics under visible-light irradiation. Under red-light irradiation having a light intensity of 5 mW mm –2, it exhibits enhanced optoelectronic characteristics including photoresponsivity, photosensitivity, and detectivity from 19.6 to 3.46 × 10 2 A/W, 4.95 to 1.46 × 10 8, and 1.45 × 10 7 to 2.13 × 10 12 Jones compared to that without a Bi 2 Te 3 layer, respectively. To confirm reproducibility and uniformity, we fabricated and compared the optoelectronic characteristics of 5 samples from batch to batch. Regarding optoelectronic neuromorphic characteristics, the ONT exhibits short- and long-term memory and a highly linear relationship between peak synaptic current and pulse number under red-light pulses. The paired-pulse facilitation index was 208%. To confirm the applicability of artificial visual perception, a 6 × 6 ONT array demonstrates long-term memory characteristic successfully after red-light irradiation with a Y-shaped pattern. In view of stability and reliability, we conducted three potentiation processes and compared the peak synaptic currents of each potentiation process.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
Funding
Ministry of Science and ICT, South Korea
References (51)
Show source-provided references
1. 10.1002/adfm.20211305310.1002/adfm.202113053 ↗
2. 10.1021/acsaelm.3c0069810.1021/acsaelm.3c00698 ↗
3. 10.1016/j.nanoen.2020.10540310.1016/j.nanoen.2020.105403 ↗
4. 10.1002/smll.20230119610.1002/smll.202301196 ↗
5. 10.1021/acsaelm.1c0101010.1021/acsaelm.1c01010 ↗
6. 10.1039/d0mh01472a10.1039/d0mh01472a ↗
7. 10.1039/D1TC05019B10.1039/D1TC05019B ↗
8. 10.1002/adma.20190643310.1002/adma.201906433 ↗
9. 10.1002/pssr.20230049010.1002/pssr.202300490 ↗
10. 10.1002/adfm.20230595910.1002/adfm.202305959 ↗
11. 10.1002/adom.20220055810.1002/adom.202200558 ↗
12. 10.1021/acsnano.1c0675810.1021/acsnano.1c06758 ↗
13. 10.1021/acsaelm.3c0115810.1021/acsaelm.3c01158 ↗
14. 10.1038/nmat325610.1038/nmat3256 ↗
15. 10.1016/j.sse.2017.10.02810.1016/j.sse.2017.10.028 ↗
16. 10.1063/5.008677210.1063/5.0086772 ↗
17. 10.1021/acsaelm.3c0154910.1021/acsaelm.3c01549 ↗
18. 10.1080/15980316.2021.193322310.1080/15980316.2021.1933223 ↗
19. 10.1080/15980316.2023.229246510.1080/15980316.2023.2292465 ↗
20. 10.1002/adma.20210897910.1002/adma.202108979 ↗
21. 10.1002/admt.20200051410.1002/admt.202000514 ↗
22. 10.1007/s12274-022-4806-410.1007/s12274-022-4806-4 ↗
23. 10.1016/j.nanoen.2019.06.00710.1016/j.nanoen.2019.06.007 ↗
24. 10.1016/j.physb.2023.41546710.1016/j.physb.2023.415467 ↗
25. 10.1016/j.tsf.2009.08.03810.1016/j.tsf.2009.08.038 ↗
26. 10.1016/j.tsf.2019.01.01010.1016/j.tsf.2019.01.010 ↗
27. 10.1039/C6TC01453D10.1039/C6TC01453D ↗
28. 10.1002/adom.20220220810.1002/adom.202202208 ↗
29. 10.1021/acsnano.8b0805610.1021/acsnano.8b08056 ↗
30. 10.1021/nn506920z10.1021/nn506920z ↗
31. 10.1002/advs.20220546010.1002/advs.202205460 ↗
32. 10.3390/nano909127310.3390/nano9091273 ↗
33. 10.1063/5.007305610.1063/5.0073056 ↗
34. 10.1021/acsaelm.4c0075210.1021/acsaelm.4c00752 ↗
35. 10.1002/adom.20220018810.1002/adom.202200188 ↗
36. 10.1002/adom.20210072510.1002/adom.202100725 ↗
37. 10.1002/aelm.20180082410.1002/aelm.201800824 ↗
38. 10.1021/acsami.9b1415410.1021/acsami.9b14154 ↗
39. 10.1021/acsami.9b2216510.1021/acsami.9b22165 ↗
40. 10.1039/C9TC04982G10.1039/C9TC04982G ↗
41. 10.1088/0953-8984/12/26/30710.1088/0953-8984/12/26/307 ↗
42. 10.1016/j.ensm.2021.06.00410.1016/j.ensm.2021.06.004 ↗
43. 10.1021/acsami.2c1749510.1021/acsami.2c17495 ↗
44. 10.1021/acsami.2c0061610.1021/acsami.2c00616 ↗
45. 10.1002/adom.20180066010.1002/adom.201800660 ↗
46. 10.1002/admi.20200088210.1002/admi.202000882 ↗
47. 10.1002/adma.20160006910.1002/adma.201600069 ↗
48. 10.1016/j.jallcom.2024.17427710.1016/j.jallcom.2024.174277 ↗
49. 10.1002/admi.20180050510.1002/admi.201800505 ↗
50. 10.1002/adma.20220849710.1002/adma.202208497 ↗
51. 10.1016/j.apsusc.2022.15553210.1016/j.apsusc.2022.155532 ↗