journal article·2023-06-09

HfOx-Based Conductive Bridge Random Access Memory with Al2O3 Sandglass Nanostructures via Glancing Angle Deposition Technology toward Neuromorphic Applications

Ying-Chun Shen, Yu-Wen Huang, Tzu-Yi Yang, Yi-Jen Yu, Hao-Chung Kuo, Tseung-Yuen Tseng, Yu-Lun Chueh

ACS Applied Nano Materials· Volume 6· Issue 11
Publisher page

Abstract

Conductive bridge random access memory (CBRAM) is one of the promising nonvolatile memories for next-generation technology owing to its high density, low power consumption, and fast switching speed, which is also a potential candidate for implementation of neuromorphic computing. However, CBRAMs suffer from stochastically growing conducting filaments in the insulator layer. Herein, we demonstrated Al 2 O 3 sandglass nanostructures (SNGSs) embedded into HfO x -based CBRAMs via glancing angle deposition technology with the AlN thermal enhanced layer to prevent the overinjection of cations and localize the growth of conducting filaments in the HfO x switching layer. With the assistance of Al 2 O 3 SNGSs and the AlN layer, the Cu/Al 2 O 3 SNGSs/HfO x /AlN/TiN device exhibited a stable on/off ratio of >10 for more than 6000 cycles. Furthermore, with a Te top electrode, the Te/Al 2 O 3 SNGSs/HfO x /AlN/TiN device shows a multilevel cell characteristic by controlling compliance currents. In addition, it possesses excellent potentiation and depression nonlinearities of 1.28 and 0.4, respectively, which is beneficial for future applications in neuromorphic computing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Perovskite Materials and Applications

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

Affiliations

Department of Materials Science and Engineering, National Tsing-Hua University, Hsinchu 30013, Taiwan

College of Semiconductor Research, National Tsing-Hua University, Hsinchu 30013, Taiwan

Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan

Department of Physics, National Sun Yat-sen University, Kaohsiung 80424, Taiwan

Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Instrument Center, National Tsing Hua University, Hsinchu 30013, Taiwan

Department of Photonics and Institute of Electro-Optical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Funding

Ministry of Science and Technology, Taiwan

References (39)

Show source-provided references
1. 10.1038/s41565-020-0655-z10.1038/s41565-020-0655-z ↗
2. 10.1103/PhysRevLett.92.17830210.1103/PhysRevLett.92.178302 ↗
3. 10.1007/s00339-011-6264-910.1007/s00339-011-6264-9 ↗
4. 10.1038/nmat202310.1038/nmat2023 ↗
5. Shimeng Yu, B. G.; Fang, Z.; Yu, H.; Kang, J.; Philip Wong, H.S. In A Neuromorphic Visual System Using RRAM Synaptic Devices with Sub-pJ Energy and Tolerance to Variability: Experimental Characterization and Large-Scale Modeling, 2012 International Electron Devices Meeting; IEEE: San Francisco, CA, USA, 2012; pp 10.4.1–10.4.4.
6. 10.1039/C9NR02027F10.1039/C9NR02027F ↗
7. 10.1109/LED.2018.286907210.1109/LED.2018.2869072 ↗
8. 10.1021/nl904092h10.1021/nl904092h ↗
9. 10.1088/0268-1242/31/6/06300210.1088/0268-1242/31/6/063002 ↗
10. 10.1016/S1002-0071(12)60001-X10.1016/S1002-0071(12)60001-X ↗
11. 10.1557/mrs.2012.510.1557/mrs.2012.5 ↗
12. 10.1038/nmat305410.1038/nmat3054 ↗
13. 10.1186/s11671-020-03299-910.1186/s11671-020-03299-9 ↗
14. 10.1021/acsomega.0c0079510.1021/acsomega.0c00795 ↗
15. 10.1002/adma.20120368010.1002/adma.201203680 ↗
16. 10.1063/5.007352810.1063/5.0073528 ↗
17. 10.1039/D0TC03639K10.1039/D0TC03639K ↗
18. 10.1002/aelm.20160023310.1002/aelm.201600233 ↗
19. 10.1007/s40843-021-1956-910.1007/s40843-021-1956-9 ↗
20. 10.1063/1.481221910.1063/1.4812219 ↗
21. 10.1016/j.carbon.2014.04.09110.1016/j.carbon.2014.04.091 ↗
22. 10.1002/smll.20160394810.1002/smll.201603948 ↗
23. 10.1002/adma.20170519310.1002/adma.201705193 ↗
24. 10.1021/acsami.1c1810110.1021/acsami.1c18101 ↗
25. 10.1116/1.58056210.1116/1.580562 ↗
26. 10.1116/1.59001910.1116/1.590019 ↗
27. 10.1021/acsmaterialslett.1c0051310.1021/acsmaterialslett.1c00513 ↗
28. 10.1002/smll.20210416810.1002/smll.202104168 ↗
29. 10.1109/LED.2016.260288610.1109/LED.2016.2602886 ↗
30. 10.1063/1.502104410.1063/1.5021044 ↗
31. 10.3390/electronics403058610.3390/electronics4030586 ↗
32. 10.1186/1556-276X-9-36610.1186/1556-276X-9-366 ↗
33. 10.1063/1.494130310.1063/1.4941303 ↗
34. 10.1038/s41467-018-07682-010.1038/s41467-018-07682-0 ↗
35. 10.1149/1.370089610.1149/1.3700896 ↗
36. Chen, P.Y.; Lin, B.; Wang, I.T.; Hou, T.H.; Ye, J. In Mitigating Effects of Non-Ideal Synaptic Device characteristics for On-Chip Learning, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2015; pp 194–199.10.1109/ICCAD.2015.7372570 ↗
37. 10.1021/acsnano.5b0794310.1021/acsnano.5b07943 ↗
38. 10.1002/smll.20200322410.1002/smll.202003224 ↗
39. 10.1021/acsami.7b1018810.1021/acsami.7b10188 ↗