A Low-Voltage Stretchable Synaptic Transistor Array for Temperature Perception, Facilitated Associative Learning, and Neuromorphic Computing
Source abstract
Abstract
Stretchable synaptic transistors are promising candidates for brain-inspired neuromorphic systems in soft robotics and wearable electronics, where temperature perception and low-power operation are critical for biological fidelity and energy efficiency. However, the interplay between mechanical strain, temperature perception, and synaptic properties remains underexplored in such devices. Here, we report a high-density, temperature-modulated stretchable synaptic transistor (TM-SST) array fabricated via a photolithography-based, transfer-free process, integrating a semiconductor carbon nanotube (s-CNT) network channel and an SU-8 dielectric layer. The devices exhibit a high on–off ratio (∼10 5 ) at a low gate voltage ( V gs ) between ±2.5 V and a drain-to-source voltage ( V ds ) of −0.1 V. Importantly, the devices exhibit temperature-dependent synaptic characteristics across 10–40 °C, with effective modulation of postsynaptic current (PSC), plasticity, memory retention, and paired-pulse facilitation (PPF), while maintaining stable performance under 40% strain. Furthermore, temperature modulation enhances neuromorphic performance: a 15 °C cooling improves memory retention in associative learning from seconds to minutes, while simulations show accelerated learning with a 10× dynamic range. This work advances stretchable synaptic devices by enabling temperature perception to enhance neuromorphic functionality.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Advanced Sensor and Energy Harvesting Materials · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ming Hsieh Department of Electrical Engineering
University of Southern California
Mork Family Department of Chemical Engineering and Materials Science
Funding
King Abdulaziz City for Science and Technology
References (58)
Show source-provided references
1. 10.1016/j.cell.2008.10.00810.1016/j.cell.2008.10.008 ↗
2. 10.1038/s41928-020-0435-710.1038/s41928-020-0435-7 ↗
3. 10.1021/acsmaterialslett.3c0060010.1021/acsmaterialslett.3c00600 ↗
4. 10.1021/acsnano.4c0042410.1021/acsnano.4c00424 ↗
5. 10.1002/adma.20220972810.1002/adma.202209728 ↗
6. 10.1007/s12274-022-4122-z10.1007/s12274-022-4122-z ↗
7. 10.1126/sciadv.aat738710.1126/sciadv.aat7387 ↗
8. 10.1002/smll.20231001310.1002/smll.202310013 ↗
9. 10.1021/acsami.6b0886610.1021/acsami.6b08866 ↗
10. 10.1021/acsami.9b1774210.1021/acsami.9b17742 ↗
11. 10.1021/acsnano.1c0429810.1021/acsnano.1c04298 ↗
12. 10.1016/j.apmt.2024.10223410.1016/j.apmt.2024.102234 ↗
13. 10.1016/j.nanoen.2020.10495210.1016/j.nanoen.2020.104952 ↗
14. 10.1002/adfm.20190110710.1002/adfm.201901107 ↗
15. 10.1016/j.neuron.2018.02.02210.1016/j.neuron.2018.02.022 ↗
16. 10.1016/j.neubiorev.2009.10.00310.1016/j.neubiorev.2009.10.003 ↗
17. 10.1021/acsami.4c0380310.1021/acsami.4c03803 ↗
18. 10.1007/s11432-021-3326-610.1007/s11432-021-3326-6 ↗
19. 10.1021/acsami.9b1722710.1021/acsami.9b17227 ↗
20. 10.1109/LED.2023.333096110.1109/LED.2023.3330961 ↗
21. 10.1021/acsnano.2c0291310.1021/acsnano.2c02913 ↗
22. 10.1021/acsnano.2c0208910.1021/acsnano.2c02089 ↗
23. 10.1007/s12274-023-5456-x10.1007/s12274-023-5456-x ↗
24. 10.1002/adfm.20230397010.1002/adfm.202303970 ↗
25. 10.1038/s41928-023-00966-410.1038/s41928-023-00966-4 ↗
26. 10.1126/sciadv.aax496110.1126/sciadv.aax4961 ↗
27. 10.1021/acsnano.1c0848210.1021/acsnano.1c08482 ↗
28. 10.1016/j.matt.2022.07.01610.1016/j.matt.2022.07.016 ↗
29. 10.1021/acsnano.4c1687410.1021/acsnano.4c16874 ↗
30. 10.1038/s41467-024-47184-w10.1038/s41467-024-47184-w ↗
31. 10.1007/s12274-021-3567-910.1007/s12274-021-3567-9 ↗
32. 10.1016/j.carbon.2021.12.08110.1016/j.carbon.2021.12.081 ↗
33. 10.26599/NR.2025.9490705910.26599/NR.2025.94907059 ↗
34. 10.1038/s41467-019-10569-310.1038/s41467-019-10569-3 ↗
35. 10.1007/s12274-021-3602-x10.1007/s12274-021-3602-x ↗
36. 10.1021/acsnano.7b0011310.1021/acsnano.7b00113 ↗
37. 10.1002/adma.20220406610.1002/adma.202204066 ↗
38. 10.1039/D2NR07209B10.1039/D2NR07209B ↗
39. 10.1021/acsnano.4c0016610.1021/acsnano.4c00166 ↗
40. 10.1021/acsami.7b0166610.1021/acsami.7b01666 ↗
41. Neamen D. A. · 2012
42. 10.1073/pnas.132004511110.1073/pnas.1320045111 ↗
43. 10.1063/1.502127410.1063/1.5021274 ↗
44. 10.1021/acsami.9b0923510.1021/acsami.9b09235 ↗
45. 10.1002/adfm.20100256310.1002/adfm.201002563 ↗
46. 10.1002/adfm.20140240710.1002/adfm.201402407 ↗
47. 10.1016/j.orgel.2008.05.02310.1016/j.orgel.2008.05.023 ↗
48. 10.1063/1.185621410.1063/1.1856214 ↗
49. 10.1021/acsanm.2c0488510.1021/acsanm.2c04885 ↗
50. 10.1038/s41928-020-00525-110.1038/s41928-020-00525-1 ↗
51. 10.1126/sciadv.aav965310.1126/sciadv.aav9653 ↗
52. 10.1016/j.mechmat.2008.02.00510.1016/j.mechmat.2008.02.005 ↗
53. 10.1039/C1JM15154A10.1039/C1JM15154A ↗
54. 10.1021/acsaelm.2c0039510.1021/acsaelm.2c00395 ↗
55. 10.1002/adfm.20180402510.1002/adfm.201804025 ↗
56. Chen, P.Y.; Peng, X.; Yu, S. NeuroSim+: An Integrated Device-to-Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures. In 2017 IEEE International Electron Devices Meeting (IEDM) ; IEEE: San Francisco, CA, USA, 2017.10.1109/IEDM.2017.8268337 ↗
57. Luo, Y.; Peng, X.; Yu, S. MLP+NeuroSimV3.0: Improving On-Chip Learning Performance with Device to Algorithm Optimizations. In Proceedings of the International Conference on Neuromorphic Systems ; ACM: Knoxville TN USA, 2019; pp 1–7.10.1145/3354265.3354266 ↗
58. 10.1109/MDAT.2018.289022910.1109/MDAT.2018.2890229 ↗