journal article·2022-12Open access

Neuromorphic devices based on fluorite‐structured ferroelectrics

Dong Hyun Lee, Geun Hyeong Park, Se Hyun Kim, Ju Yong Park, Kun Yang, Stefan Slesazeck, Thomas Mikolajick, Min Hyuk Park

InfoMat· Volume 4· Issue 12

Abstract

Abstract A continuous exponential rise has been observed in the storage and processing of the data that may not curtail in the foreseeable future. The required data processing speed and power consumption are restricted by the buses between the logic and memory devices that are characteristic of the von Neumann computing architecture. Bio‐mimicking neuromorphic computing has garnered considerable academic and industrial interest to resolve these challenges. Additionally, devices based on emerging nonvolatile memories capable of mimicking the behaviors of synapses and neurons, which are the main elements in biological computing systems (brains), are attracting significant interest from the device community. With the discovery of ferroelectricity in fluorite‐structured oxides, such as HfO2and ZrO2, which are compatible with the state‐of‐the‐art complementary‐metal‐oxide‐semiconductor processes, ferroelectric devices have rapidly evolved as the main direction of these research and development activities. Fundamental science related to fluorite‐structured ferroelectrics has been intensively studied over the last decade. At present, the focus is gradually moving to practical applications, including neuromorphic computing and advanced classical processing or memory units in the conventional von Neumann architecture. However, despite its rapid development, the wealth of recent progress in neuromorphic computing devices based on fluorite‐structured ferroelectrics has not been reviewed and systemized. This progress report comprehensively reviews and systemizes the recent progress in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite‐structured ferroelectrics. image
Source: crossref · 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 Materials Science and Engineering & Inter‐University Semiconductor Research Center, College of Engineering Seoul National University Seoul Republic of Korea

Research Institute of Advanced Materials Seoul National University Seoul Republic of Korea

NaMLab gGmbH Dresden Germany

Institute of Engineering Research Seoul National University Seoul Republic of Korea

Funding

National Research Foundation · European Commission

References (100+)

Show source-provided references
1. 10.1038/s41586-020-2782-y10.1038/s41586-020-2782-y ↗
2. 10.1088/1361-6528/aba70f10.1088/1361-6528/aba70f ↗
3. 10.1088/2634-4386/ac4a8310.1088/2634-4386/ac4a83 ↗
4. 10.1196/annals.1292.01010.1196/annals.1292.010 ↗
5. 10.1088/0957-4484/20/34/34520110.1088/0957-4484/20/34/345201 ↗
6. 10.1088/0957-4484/24/38/38401010.1088/0957-4484/24/38/384010 ↗
7. 10.1038/nature1444110.1038/nature14441 ↗
8. 10.1109/5.5835610.1109/5.58356 ↗
9. Neuromorphic silicon neuron circuits
10. 10.1088/1361-6528/aae97510.1088/1361-6528/aae975 ↗
11. 10.1002/adfm.20210120110.1002/adfm.202101201 ↗
12. 10.1149/1.174078510.1149/1.1740785 ↗
13. KooJ‐M SeoB‐S KimS et al.Fabrication of 3D trench PZT capacitors for 256Mbit FRAM device application. IEEE International Electron Devices Meeting 2005. IEDM Technical Digest;2005;4:343.10.1109/IEDM.2005.1609345 ↗
14. 10.1063/1.363405210.1063/1.3634052 ↗
15. 10.1557/mrc.2018.17510.1557/mrc.2018.175 ↗
16. 10.1002/adma.20140453110.1002/adma.201404531 ↗
17. 10.1557/mrs.2018.9210.1557/mrs.2018.92 ↗
18. 10.1063/5.003554210.1063/5.0035542 ↗
19. 10.1063/5.004797710.1063/5.0047977 ↗
20. 10.1021/acsaelm.1c0079210.1021/acsaelm.1c00792 ↗
21. 10.1039/D0TC01695K10.1039/D0TC01695K ↗
22. 10.1109/IEDM.2013.672460510.1109/IEDM.2013.6724605 ↗
23. 10.1109/IEDM.2011.613160610.1109/IEDM.2011.6131606 ↗
24. MüllerJ YurchukE SchlösserT et al.Ferroelectricity in HfO2enables nonvolatile data storage in 28 nm HKMG. Paper presented at 2012 Symposium on VLSI Technology (VLSIT);2012:25–26.10.1109/VLSIT.2012.6242443 ↗
25. DünkelS TrentzschM RichterR et al.A FeFET based super‐low‐power ultra‐fast embedded NVM technology for 22nm FDSOI and beyond. Paper presented at 2017 IEEE International Electron Devices Meeting (IEDM)2017:19.7.1–19.7.4.10.1109/IEDM.2017.8268425 ↗
26. BeyerS DünkelS TrentzschM et al.FeFET: A versatile CMOS compatible device with game‐changing potential. Paper presented at 2020 IEEE International Memory Workshop (IMW);2020:1–4.10.1109/IMW48823.2020.9108150 ↗
27. 10.1109/LED.2021.308321910.1109/LED.2021.3083219 ↗
28. 10.1109/JEDS.2021.312927910.1109/JEDS.2021.3129279 ↗
29. 10.1109/TED.2021.313836010.1109/TED.2021.3138360 ↗
30. FujiiS KamimutaY InoT NakasakiY TakaishiR SaitohM.First demonstration and performance improvement of ferroelectric HfO2‐based resistive switch with low operation current and intrinsic diode property. Paper presented at 2016 IEEE Symposium on VLSI Technology;2016:1–2.10.1109/VLSIT.2016.7573413 ↗
31. 10.1038/s41586-020-2208-x10.1038/s41586-020-2208-x ↗
32. 10.1126/science.aba006710.1126/science.aba0067 ↗
33. 10.1021/acs.nanolett.7b0398810.1021/acs.nanolett.7b03988 ↗
34. 10.1126/science.abm864210.1126/science.abm8642 ↗
35. 10.1002/adma.20210988910.1002/adma.202109889 ↗
36. 10.1039/D1CS00886B10.1039/D1CS00886B ↗
37. 10.1557/s43577-021-00210-410.1557/s43577-021-00210-4 ↗
38. 10.1109/TED.2020.297614810.1109/TED.2020.2976148 ↗
39. 10.1021/nl302049k10.1021/nl302049k ↗
40. 10.1039/C7NR02121F10.1039/C7NR02121F ↗
41. 10.1063/1.493558810.1063/1.4935588 ↗
42. 10.1021/acsami.1c0871810.1021/acsami.1c08718 ↗
43. 10.1021/acsami.8b1955810.1021/acsami.8b19558 ↗
44. 10.1063/1.501709410.1063/1.5017094 ↗
45. 10.1016/j.actamat.2021.11669610.1016/j.actamat.2021.116696 ↗
46. 10.1080/1478643620821447110.1080/14786436208214471 ↗
47. 10.1109/LED.2017.277626310.1109/LED.2017.2776263 ↗
48. MullerJ PolakowskiP MullerS et al.High endurance strategies for hafnium oxide based ferroelectric field effect transistor. Paper presented at 2016 16th Non‐Volatile Memory Technology Symposium (NVMTS);2016:1–7.10.1109/NVMTS.2016.7781517 ↗
49. 10.1109/LED.2016.259362710.1109/LED.2016.2593627 ↗
50. 10.1002/aelm.20180052210.1002/aelm.201800522 ↗
51. 10.1039/C7NR06342C10.1039/C7NR06342C ↗
52. 10.1039/C9NR05768D10.1039/C9NR05768D ↗
53. 10.7567/APEX.9.09150110.7567/APEX.9.091501 ↗
54. 10.1111/j.1151-2916.2001.tb00843.x10.1111/j.1151-2916.2001.tb00843.x ↗
55. 10.1103/PhysRevB.63.17410810.1103/PhysRevB.63.174108 ↗
56. 10.1063/1.491670710.1063/1.4916707 ↗
57. 10.1063/1.363643410.1063/1.3636434 ↗
58. 10.1063/1.491913510.1063/1.4919135 ↗
59. 10.1002/aelm.20160017310.1002/aelm.201600173 ↗
60. 10.1063/1.481148310.1063/1.4811483 ↗
61. 10.1063/1.486600810.1063/1.4866008 ↗
62. 10.1039/C7TC01200D10.1039/C7TC01200D ↗
63. 10.1002/admi.20170125810.1002/admi.201701258 ↗
64. 10.1038/s41928-020-00492-710.1038/s41928-020-00492-7 ↗
65. 10.1063/1.490207210.1063/1.4902072 ↗
66. 10.1039/C6TC02003H10.1039/C6TC02003H ↗
67. 10.1016/j.nanoen.2017.07.01710.1016/j.nanoen.2017.07.017 ↗
68. 10.1016/j.actamat.2021.11740510.1016/j.actamat.2021.117405 ↗
69. 10.1002/admi.20190004210.1002/admi.201900042 ↗
70. 10.7567/1347-4065/ab45e310.7567/1347-4065/ab45e3 ↗
71. 10.1021/acsaelm.0c0068010.1021/acsaelm.0c00680 ↗
72. 10.1063/1.502642410.1063/1.5026424 ↗
73. 10.1063/1.503056210.1063/1.5030562 ↗
74. 10.1063/5.003510010.1063/5.0035100 ↗
75. ChungW SiM PeideDY.First demonstration of Ge ferroelectric nanowire FET as synaptic device for online learning in neural network with high number of conductance state and Gmax/Gmin. Paper presented at 2018 IEEE International Electron Devices Meeting (IEDM);2018:15.2.1–15.2.4.10.1109/IEDM.2018.8614516 ↗
76. 10.1109/TED.2021.310000510.1109/TED.2021.3100005 ↗
77. 10.1143/JPSJ.63.103110.1143/JPSJ.63.1031 ↗
78. On the statistical theory of the crystallization of metals
79. 10.1063/1.175063110.1063/1.1750631 ↗
80. 10.1080/0015019960823024710.1080/00150199608230247 ↗
81. 10.1080/1058458970801304610.1080/10584589708013046 ↗
82. 10.1103/PhysRev.95.69010.1103/PhysRev.95.690 ↗
83. 10.1109/TED.2020.300962310.1109/TED.2020.3009623 ↗
84. 10.1109/TED.2020.300995610.1109/TED.2020.3009956 ↗
85. 10.1088/1361-6641/ac1b1110.1088/1361-6641/ac1b11 ↗
86. 10.23919/VLSIT.2019.877648710.23919/VLSIT.2019.8776487 ↗
87. 10.1021/acsami.9b1287810.1021/acsami.9b12878 ↗
88. 10.1021/acsami.0c0087710.1021/acsami.0c00877 ↗
89. 10.1063/1.510856210.1063/1.5108562 ↗
90. 10.1088/1361-6528/ab554b10.1088/1361-6528/ab554b ↗
91. 10.1017/CBO978110744761510.1017/CBO9781107447615 ↗
92. 10.1080/23746149.2016.125958510.1080/23746149.2016.1259585 ↗
93. 10.1109/82.84211010.1109/82.842110 ↗
94. 10.1038/s41586-019-1677-210.1038/s41586-019-1677-2 ↗
95. MulaosmanovicH OckerJ MüllerS et al.Novel ferroelectric FET based synapse for neuromorphic systems. Paper presented at 2017 Symposium on VLSI Technology;2017:T176–T177.10.23919/VLSIT.2017.7998165 ↗
96. 10.1021/acsaelm.0c0083210.1021/acsaelm.0c00832 ↗
97. SureshB BerteleM BreyerET et al.Simulation of integrate‐and‐fire neuron circuits using HfO2‐based ferroelectric field effect transistors. Paper presented at 2019 26th IEEE International Conference on Electronics Circuits and Systems (ICECS);2019:229–232.10.1109/ICECS46596.2019.8965004 ↗
98. 10.1039/C8NR07135G10.1039/C8NR07135G ↗
99. 10.1038/s41598-019-44631-310.1038/s41598-019-44631-3 ↗
100. 10.1039/C8NR04734K10.1039/C8NR04734K ↗