journal article·2021-07-21

Tunable Synaptic Characteristics of a Ti/TiO2/Si Memory Device for Reservoir Computing

Jinwoong Yang, Hyojong Cho, Hojeong Ryu, Muhammad Ismail, Chandreswar Mahata, Sungjun Kim

ACS Applied Materials & Interfaces· Volume 13· Issue 28
Publisher page

Abstract

In this study, we fabricate and characterize a Ti/TiO2/Si device with different dopant concentrations on a silicon surface for neuromorphic systems. We verify the device stack using transmission electron microscopy (TEM). The Ti/TiO2/p++Si device exhibits interface-type bipolar resistive switching with long-term memory. The potentiation and depression by the pulses of various amplitudes are demonstrated using gradual resistive switching. Moreover, pattern-recognition accuracy (>85%) is obtained in the neuromorphic system simulation when conductance is used as the weight in the network. Next, we investigate the short-term memory characteristics of the Ti/TiO2/p+Si device. The dynamic range is well-controlled by the pulse amplitude, and the conductance decay depends on the interval between the pulses. Finally, we build a reservoir computing system using the short-term effect of the Ti/TiO2/p+Si device, in which 4 bits (16 states) are differentiated by various pulse streams through the device that can be used for pattern recognition.
Source: openalex_inverted_index · 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

Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea

Funding

National Research Foundation of Korea

References (41)

Show source-provided references
1. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
2. 10.1109/TED.2017.274634210.1109/TED.2017.2746342 ↗
3. 10.1116/1.330157910.1116/1.3301579 ↗
4. 10.1088/0022-3727/46/7/07400110.1088/0022-3727/46/7/074001 ↗
5. 10.1016/S1369-7021(08)70119-610.1016/S1369-7021(08)70119-6 ↗
6. 10.1038/nature0693210.1038/nature06932 ↗
7. 10.1088/0268-1242/31/6/06300210.1088/0268-1242/31/6/063002 ↗
8. 10.1039/C8FD00112J10.1039/C8FD00112J ↗
9. 10.1038/srep1331110.1038/srep13311 ↗
10. 10.1126/sciadv.aaz907910.1126/sciadv.aaz9079 ↗
11. 10.1038/s41565-020-0694-510.1038/s41565-020-0694-5 ↗
12. 10.3389/fnins.2020.0035810.3389/fnins.2020.00358 ↗
13. 10.3389/fnins.2020.0008810.3389/fnins.2020.00088 ↗
14. 10.1088/1361-6528/ab4a6d10.1088/1361-6528/ab4a6d ↗
15. 10.1016/j.neunet.2020.11.00510.1016/j.neunet.2020.11.005 ↗
16. 10.1039/C8CP03945C10.1039/C8CP03945C ↗
17. 10.1038/nmat475610.1038/nmat4756 ↗
18. 10.1016/j.jallcom.2020.15667510.1016/j.jallcom.2020.156675 ↗
19. 10.1021/acsami.0c0786710.1021/acsami.0c07867 ↗
20. 10.1002/pssr.20190020410.1002/pssr.201900204 ↗
21. 10.1021/nn202983n10.1021/nn202983n ↗
22. 10.1021/acsami.7b1119110.1021/acsami.7b11191 ↗
23. 10.1021/acsaelm.9b0057210.1021/acsaelm.9b00572 ↗
24. 10.1063/1.494136410.1063/1.4941364 ↗
25. 10.1016/j.nanoen.2020.10461610.1016/j.nanoen.2020.104616 ↗
26. 10.1016/j.neunet.2019.03.00510.1016/j.neunet.2019.03.005 ↗
27. 10.1038/s41467-017-02337-y10.1038/s41467-017-02337-y ↗
28. 10.1063/1.495271910.1063/1.4952719 ↗
29. 10.1021/acsami.7b0292110.1021/acsami.7b02921 ↗
30. 10.1063/1.497567610.1063/1.4975676 ↗
31. 10.1016/j.apsusc.2010.07.08610.1016/j.apsusc.2010.07.086 ↗
32. 10.1007/s10854-009-9941-010.1007/s10854-009-9941-0 ↗
33. 10.1088/0022-3727/42/3/03530810.1088/0022-3727/42/3/035308 ↗
34. 10.1039/C4RA09443C10.1039/C4RA09443C ↗
35. 10.1021/acsami.7b0352710.1021/acsami.7b03527 ↗
36. 10.1088/1361-6463/aa516110.1088/1361-6463/aa5161 ↗
37. 10.1109/TED.2018.288129410.1109/TED.2018.2881294 ↗
38. 10.1109/LED.2018.279997310.1109/LED.2018.2799973 ↗
39. 10.1109/TED.2017.271749710.1109/TED.2017.2717497 ↗
40. 10.1116/1.490572710.1116/1.4905727 ↗
41. 10.1088/0957-4484/26/3/03520310.1088/0957-4484/26/3/035203 ↗