journal article·2017-12-26

Extremely Low Operating Current Resistive Memory Based on Exfoliated 2D Perovskite Single Crystals for Neuromorphic Computing

He Tian, Lianfeng Zhao, Xuefeng Wang, Yao-Wen Yeh, Nan Yao, Barry P. Rand, Tian-Ling Ren

ACS Nano· Volume 11· Issue 12
Publisher page

Abstract

Extremely low energy consumption neuromorphic computing is required to achieve massively parallel information processing on par with the human brain. To achieve this goal, resistive memories based on materials with ionic transport and extremely low operating current are required. Extremely low operating current allows for low power operation by minimizing the program, erase, and read currents. However, materials currently used in resistive memories, such as defective HfO x, AlO x, TaO x, etc., cannot suppress electronic transport ( i . e ., leakage current) while allowing good ionic transport. Here, we show that 2D Ruddlesden–Popper phase hybrid lead bromide perovskite single crystals are promising materials for low operating current nanodevice applications because of their mixed electronic and ionic transport and ease of fabrication. Ionic transport in the exfoliated 2D perovskite layer is evident via the migration of bromide ions. Filaments with a diameter of approximately 20 nm are visualized, and resistive memories with extremely low program current down to 10 pA are achieved, a value at least 1 order of magnitude lower than conventional materials. The ionic migration and diffusion as an artificial synapse is realized in the 2D layered perovskites at the pA level, which can enable extremely low energy neuromorphic computing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Perovskite Materials and Applications · Transition Metal Oxide Nanomaterials

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

Affiliations

Institute of Microelectronics and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China

Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544, United States

Princeton Institute for Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, United States

Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States

Funding

Ministry of Science and Technology of the People's Republic of China · Office of Naval Research · Ministry of Agriculture of the People's Republic of China · Tsinghua University · National Natural Science Foundation of China · Advanced Sensor and Integrated System Lab, Tsinghua University

References (47)

Show source-provided references
1. 10.1038/23610.1038/236 ↗
2. 10.1126/science.125464210.1126/science.1254642 ↗
3. 10.1038/nature0301010.1038/nature03010 ↗
4. 10.1038/nmat305410.1038/nmat3054 ↗
5. 10.1038/nature1444110.1038/nature14441 ↗
6. 10.1038/nmat475610.1038/nmat4756 ↗
7. 10.1038/nmat485610.1038/nmat4856 ↗
8. 10.1038/nature0693210.1038/nature06932 ↗
9. 10.1038/nnano.2009.45610.1038/nnano.2009.456 ↗
10. 10.1109/LED.2013.229437510.1109/LED.2013.2294375 ↗
11. 10.1038/nenergy.2016.15210.1038/nenergy.2016.152 ↗
12. 10.1021/acsenergylett.6b0032010.1021/acsenergylett.6b00320 ↗
13. 10.1038/nphoton.2016.26910.1038/nphoton.2016.269 ↗
14. 10.1021/acsnano.7b0040410.1021/acsnano.7b00404 ↗
15. 10.1002/adma.20160531710.1002/adma.201605317 ↗
16. 10.1038/nphoton.2016.18510.1038/nphoton.2016.185 ↗
17. 10.1038/nature1830610.1038/nature18306 ↗
18. 10.1126/science.aaa272510.1126/science.aaa2725 ↗
19. 10.1126/science.aac766010.1126/science.aac7660 ↗
20. 10.1038/nmat415010.1038/nmat4150 ↗
21. 10.1039/C6EE00413J10.1039/C6EE00413J ↗
22. 10.1021/acsnano.6b0164310.1021/acsnano.6b01643 ↗
23. 10.1109/JPROC.2012.219036910.1109/JPROC.2012.2190369 ↗
24. 10.1109/LED.2014.236361810.1109/LED.2014.2363618 ↗
25. 10.1109/LED.2016.253094210.1109/LED.2016.2530942 ↗
26. 10.1016/S1369-7021(08)70119-610.1016/S1369-7021(08)70119-6 ↗
27. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
28. 10.1002/adma.20080379810.1002/adma.200803798 ↗
29. 10.1038/ncomms370710.1038/ncomms3707 ↗
30. ChiaHua, H.; Cho-Lun, H.; Chun-Chi, C.; Jan-Tsai, L.; Cheng-San, W.; Chien-Chao, H.; Chenming, H.; Fu-Liang, Y.In9nm Half-Pitch Functional Resistive Memory Cell with < 1 uA Programming Current Using Thermally Oxidized Sub-Stoichiometric WOxFilm.2010 IEEE Int. Electron Devices Meet., Dec 6–8, 2010; 2010; pp19.1.1–19.1.4.10.1109/IEDM.2010.5703389 ↗
31. 10.1109/VTSA.2010.548891610.1109/VTSA.2010.5488916 ↗
32. 10.1002/adma.20150312510.1002/adma.201503125 ↗
33. 10.1038/nmat307010.1038/nmat3070 ↗
34. 10.1021/acsnano.5b0794310.1021/acsnano.5b07943 ↗
35. 10.1021/nl304246d10.1021/nl304246d ↗
36. 10.1021/nl500591610.1021/nl5005916 ↗
37. 10.1002/adma.20160297610.1002/adma.201602976 ↗
38. 10.1038/nnano.2015.5610.1038/nnano.2015.56 ↗
39. 10.1109/LED.2010.207417710.1109/LED.2010.2074177 ↗
40. 10.1109/LED.2012.223104710.1109/LED.2012.2231047 ↗
41. 10.1002/adma.20150288910.1002/adma.201502889 ↗
42. 10.1016/j.vacuum.2016.05.01010.1016/j.vacuum.2016.05.010 ↗
43. 10.1039/C6TC02503J10.1039/C6TC02503J ↗
44. 10.1002/aelm.20160010010.1002/aelm.201600100 ↗
45. 10.1002/adma.20150636310.1002/adma.201506363 ↗
46. Yu, S.; Ximeng, G.; Wong, H. S. P.InOn the Stochastic Nature of Resistive Switching in Metal Oxide RRAM: Physical Modeling, Monte Carlo Simulation, and Experimental Characterization,2011 IEEE Int. Electron Devices Meet., Dec 5–7, 2011; 2011; pp17.3.1–17.3.4.10.1109/IEDM.2011.6131572 ↗
47. 10.1109/TED.2012.218454510.1109/TED.2012.2184545 ↗