Crystallinity‐Controlled Hexagonal Boron Nitride‐Based Memristors for Fully Integrated Reservoir Computing Processors
Source abstract
Abstract
Abstract Memristors based on 2D materials (2DMs) have attracted considerable research interest due to their excellent switching performance. Former synthesis methods for 2DMs aimed to synthesize 2DMs with a large grain size. However, these methods cause a stochastic distribution of defects in high‐density memristor arrays, resulting in device nonuniformity. Moreover, high synthesis temperatures and mechanical transfer make it difficult to implement large‐area memristor arrays and additional integration. Therefore, synthesis methods of nanocrystalline 2DMs for memristors are essential. In this study, crystallinity‐controlled hexagonal boron nitride is directly synthesized on metal electrodes, and a fully integrated memristor‐based reservoir computing processor is implemented. Memristors using nanocrystalline hexagonal boron nitride (NC h‐BN) exhibit volatile switching and reliable reservoir dynamics. Memristors using amorphous boron nitride (a‐BN) exhibit nonvolatile switching and linear potentiation/depression curves (αP = −0.475, αD = 0.656). By integrating NC h‐BN and a‐BN memristors in three dimensions, an efficient reservoir computing processor with integrated reservoir and readout layers is realized. Overall, the neural network of the integrated processor shows high accuracy for inferring temporal data. Hence, the crystallinity‐controlled hexagonal boron nitride synthesis method paves the way for the realization of fully integrated reservoir computing processors.
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
School of Electrical Engineering Graduate School of Semiconductor Technology Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro, Yuseong‐gu Daejeon 34141 Republic of Korea
Funding
National Research Foundation of Korea · Ministry of Education · Korea Institute for Advancement of Technology
References (50)
Show source-provided references
1. 10.1146/annurev-control-060117-10515710.1146/annurev-control-060117-105157 ↗
2. 10.1038/s41565-020-0655-z10.1038/s41565-020-0655-z ↗
3. 10.1002/jcc.2476410.1002/jcc.24764 ↗
4. 10.1016/j.neunet.2019.03.00510.1016/j.neunet.2019.03.005 ↗
5. 10.1038/s41467-021-25801-210.1038/s41467-021-25801-2 ↗
6. 10.1039/c3nr03472k10.1039/c3nr03472k ↗
7. 10.1038/s41467-020-20692-110.1038/s41467-020-20692-1 ↗
8. 10.1002/adfm.20220472110.1002/adfm.202204721 ↗
9. 10.1038/s41563-021-01099-910.1038/s41563-021-01099-9 ↗
10. 10.1038/s41467-023-39371-y10.1038/s41467-023-39371-y ↗
11. 10.1038/s41467-024-46323-710.1038/s41467-024-46323-7 ↗
12. 10.1038/s41928-022-00838-310.1038/s41928-022-00838-3 ↗
13. 10.1021/acs.nanolett.8b0514010.1021/acs.nanolett.8b05140 ↗
14. 10.1002/adfm.20220127610.1002/adfm.202201276 ↗
15. 10.1021/acs.nanolett.1c0316910.1021/acs.nanolett.1c03169 ↗
16. 10.1038/s41467-024-46372-y10.1038/s41467-024-46372-y ↗
17. 10.1021/acsami.0c1878410.1021/acsami.0c18784 ↗
18. 10.1002/adma.20220188010.1002/adma.202201880 ↗
19. 10.1038/s41928-021-00573-110.1038/s41928-021-00573-1 ↗
20. 10.1038/s41467-022-30519-w10.1038/s41467-022-30519-w ↗
21. 10.1002/adma.20210337610.1002/adma.202103376 ↗
22. 10.1002/smll.20230022310.1002/smll.202300223 ↗
23. 10.1038/s41928-018-0118-910.1038/s41928-018-0118-9 ↗
24. 10.1109/LED.2011.216699210.1109/LED.2011.2166992 ↗
25. 10.1038/s41928-024-01233-w10.1038/s41928-024-01233-w ↗
26. 10.1038/s41586-023-05973-110.1038/s41586-023-05973-1 ↗
27. 10.1038/s41699-018-0050-x10.1038/s41699-018-0050-x ↗
28. 10.1038/s41586-020-2375-910.1038/s41586-020-2375-9 ↗
29. 10.1109/TED.2023.325840310.1109/TED.2023.3258403 ↗
30. 10.1002/admt.20220002210.1002/admt.202200022 ↗
31. 10.1021/nn500059s10.1021/nn500059s ↗
32. 10.1002/adfm.20180755010.1002/adfm.201807550 ↗
33. 10.1002/adma.20220737410.1002/adma.202207374 ↗
34. 10.1021/acs.nanolett.7b0434210.1021/acs.nanolett.7b04342 ↗
35. 10.1002/adfm.20220174910.1002/adfm.202201749 ↗
36. 10.1002/adfm.20221126910.1002/adfm.202211269 ↗
37. 10.1021/acs.nanolett.2c0356510.1021/acs.nanolett.2c03565 ↗
38. 10.1002/adma.20220540210.1002/adma.202205402 ↗
39. 10.1038/s41467-024-48429-410.1038/s41467-024-48429-4 ↗
40. 10.1038/s41467-018-07979-010.1038/s41467-018-07979-0 ↗
41. 10.1002/aisy.20210006210.1002/aisy.202100062 ↗
42. 10.1002/adfm.20200571810.1002/adfm.202005718 ↗
43. 10.1038/s41928-023-00984-210.1038/s41928-023-00984-2 ↗
44. 10.1002/adma.20220148810.1002/adma.202201488 ↗
45. I.Boybat S.Nandakumar M. LeGallo B.Rajendran Y.Leblebici A.Sebastian E.Eleftheriou presented at2018 Non‐Volatile Memory Technology Symposium (NVMTS) Japan January 2018.
46. 10.1038/ncomms918110.1038/ncomms9181 ↗
47. 10.1002/aisy.20220001810.1002/aisy.202200018 ↗
48. 10.1109/72.84674110.1109/72.846741 ↗
49. 10.1038/s41467-017-02337-y10.1038/s41467-017-02337-y ↗
50. 10.1038/s44172-022-00021-810.1038/s44172-022-00021-8 ↗