journal article·2021-10-22Open access

Dynamic Behaviors and Training Effects in TiN/Ti/HfOx/TiN-Nanolayered Memristors with Controllable Quantized Conductance States: Implications for Quantum and Neuromorphic Computing Devices

Min-Hsuan Peng, Ching-Yang Pan, Hao-Xuan Zheng, Ting-Chang Chang, Pei-hsun Jiang

ACS Applied Nano Materials· Volume 4· Issue 10

Abstract

Controllable quantized conductance states of TiN/Ti/HfO x /TiN memristors are realized with great precision through a pulse-mode reset procedure, assisted with analytical differentiation of the conditions of the set procedure, which involves critical monitoring of the measured bias voltage. An intriguing training effect that leads to faster switching of the states is also observed during the operation. Detailed analyses of the low- and high-resistance states under different compliance currents reveal a complete picture of the structural evolution and dynamic behaviors of the conductive filament in the HfO x layer. This study provides a closer inspection on the quantum-level manipulation of nanoscale atomic configurations in the memristors, which helps to develop essential knowledge about the design and fabrication of the future memristor-based quantum devices and neuromorphic computing devices.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Physics, National Taiwan Normal University, Taipei 116, Taiwan

Department of Physics, National Sun Yat-Sen University, Kaohsiung 804, Taiwan

Funding

Ministry of Science and Technology, Taiwan

References (57)

Show source-provided references
1. 10.1109/tct.1971.108333710.1109/tct.1971.1083337 ↗
2. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
3. 10.3390/ma1301016610.3390/ma13010166 ↗
4. 10.1109/tcad.2015.244574110.1109/tcad.2015.2445741 ↗
5. 10.1088/0957-4484/27/36/36520210.1088/0957-4484/27/36/365202 ↗
6. 10.1002/adma.20180255410.1002/adma.201802554 ↗
7. 10.1063/1.323650610.1063/1.3236506 ↗
8. 10.1002/adfm.20110111710.1002/adfm.201101117 ↗
9. 10.1109/ted.2013.226447610.1109/ted.2013.2264476 ↗
10. 10.1109/led.2018.278942510.1109/led.2018.2789425 ↗
11. 10.3390/mi1104042710.3390/mi11040427 ↗
12. 10.1063/5.002001410.1063/5.0020014 ↗
13. 10.1038/srep2950710.1038/srep29507 ↗
14. 10.1186/s11671-015-1118-610.1186/s11671-015-1118-6 ↗
15. 10.1002/aelm.20180085410.1002/aelm.201800854 ↗
16. 10.1038/nmat305410.1038/nmat3054 ↗
17. 10.1038/ncomms1114210.1038/ncomms11142 ↗
18. 10.1039/d0cp03767b10.1039/d0cp03767b ↗
19. 10.1038/srep1331110.1038/srep13311 ↗
20. 10.1063/1.480226510.1063/1.4802265 ↗
21. 10.1021/acsami.7b1983610.1021/acsami.7b19836 ↗
22. 10.1063/1.280728210.1063/1.2807282 ↗
23. 10.1063/1.480282110.1063/1.4802821 ↗
24. 10.1109/tnano.2015.241177410.1109/tnano.2015.2411774 ↗
25. 10.1038/srep0270810.1038/srep02708 ↗
26. 10.1002/adfm.20160510410.1002/adfm.201605104 ↗
27. 10.1063/1.36937310.1063/1.369373 ↗
28. Kao K. C. · 1981
29. Lampert M. A. · 1970
30. 10.1063/1.172848710.1063/1.1728487 ↗
31. Mott N. F. · 1940
32. 10.1063/1.306836710.1063/1.3068367 ↗
33. Özben E. D. · 2010
34. 10.1002/pssb.222175010210.1002/pssb.2221750102 ↗
35. 10.1063/1.503857810.1063/1.5038578 ↗
36. 10.1186/s11671-020-03299-910.1186/s11671-020-03299-9 ↗
37. 10.2200/s00681ed1v01y201510eet00610.2200/s00681ed1v01y201510eet006 ↗
38. 10.1016/j.mee.2013.03.14510.1016/j.mee.2013.03.145 ↗
39. 10.1109/ted.2012.220231910.1109/ted.2012.2202319 ↗
40. 10.1038/s41598-017-11165-510.1038/s41598-017-11165-5 ↗
41. 10.1103/physrevb.94.16516010.1103/physrevb.94.165160 ↗
42. 10.1038/nnano.2009.45610.1038/nnano.2009.456 ↗
43. 10.1038/ncomms173710.1038/ncomms1737 ↗
44. 10.1021/acs.jpclett.5b0063310.1021/acs.jpclett.5b00633 ↗
45. 10.1039/c3nr03472k10.1039/c3nr03472k ↗
46. 10.1088/0268-1242/31/6/06300210.1088/0268-1242/31/6/063002 ↗
47. 10.1109/led.2016.252445010.1109/led.2016.2524450 ↗
48. 10.1088/2053-1591/aaa1fc10.1088/2053-1591/aaa1fc ↗
49. 10.1149/1.370089610.1149/1.3700896 ↗
50. 10.1039/c8fd00116b10.1039/c8fd00116b ↗
51. Volos, C.; Pham, V.T. Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications; Academic Press, 2021; p 392.
52. 10.1109/led.2016.260057410.1109/led.2016.2600574 ↗
53. 10.1021/acs.nanolett.5b0069710.1021/acs.nanolett.5b00697 ↗
54. 10.3390/nano1105126110.3390/nano11051261 ↗
55. 10.1021/acsami.9b0818910.1021/acsami.9b08189 ↗
56. Marrone, F.; Zoppo, G.; Corinto, F.; Gilli, M. Second Order Memristor Models for Neuromorphic Computing. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, 2019; pp 315–318.10.1109/MWSCAS.2019.8885287 ↗
57. 10.1002/aelm.20190105510.1002/aelm.201901055 ↗