journal article·2023-10-18

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing

Jie Luo, Guo Tian, Ding-Guo Zhang, Xing-Chen Zhang, Zhen-Ni Lu, Zhong-Da Zhang, Jia-Wei Cai, Ya-Nan Zhong, Jian-Long Xu, Xu Gao, Sui-Dong Wang

ACS Applied Materials & Interfaces· Volume 15· Issue 41
Publisher page

Abstract

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural Networks and Reservoir Computing

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

Affiliations

Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China

Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China

Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa, Macao 999078, P. R. China

Funding

Natural Science Research of Jiangsu Higher Education Institutions of China · Collaborative Innovation Center of Suzhou Nano Science and Technology · National Key Research and Development Program of China · Qinglan Project of Jiangsu Province of China · Guangzhou Municipal Science and Technology Program key projects · Fundo para o Desenvolvimento das Ciências e da Tecnologia · National Natural Science Foundation of China · Higher Education Discipline Innovation Project

References (65)

Show source-provided references
1. 10.1126/science.aam974410.1126/science.aam9744 ↗
2. 10.1038/s41565-020-0724-310.1038/s41565-020-0724-3 ↗
3. 10.1021/acsami.1c0750510.1021/acsami.1c07505 ↗
4. 10.1002/adma.20190276110.1002/adma.201902761 ↗
5. 10.1038/s41563-019-0291-x10.1038/s41563-019-0291-x ↗
6. 10.1038/s41586-019-1677-210.1038/s41586-019-1677-2 ↗
7. 10.1126/science.abg728510.1126/science.abg7285 ↗
8. 10.1021/acsnano.2c0728110.1021/acsnano.2c07281 ↗
9. 10.1038/s42254-020-0208-210.1038/s42254-020-0208-2 ↗
10. 10.1002/adma.20200361010.1002/adma.202003610 ↗
11. 10.1021/acsami.3c0009210.1021/acsami.3c00092 ↗
12. 10.1038/nn.321910.1038/nn.3219 ↗
13. 10.1021/acsaelm.9b0069410.1021/acsaelm.9b00694 ↗
14. Purves, D.; Augustine, G. J.; Fitzpatrick, D.; Hall, C. W.; LaMantia, A. S.; Mooney, R. D.; Platt, M. L.; White, L. E. Neurosciences, 6th ed. Oxford University Press; United States of America, 2018; pp 33–63.
15. 10.1002/adbi.20170019810.1002/adbi.201700198 ↗
16. 10.1002/admt.20180058910.1002/admt.201800589 ↗
17. 10.1063/1.511821710.1063/1.5118217 ↗
18. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
19. 10.1038/s41467-020-15158-310.1038/s41467-020-15158-3 ↗
20. 10.1038/s41928-021-00615-810.1038/s41928-021-00615-8 ↗
21. 10.1038/s41928-019-0270-x10.1038/s41928-019-0270-x ↗
22. 10.1021/acsami.0c0087710.1021/acsami.0c00877 ↗
23. 10.1002/adma.20170671710.1002/adma.201706717 ↗
24. 10.1038/nmat364910.1038/nmat3649 ↗
25. 10.1038/s41467-020-20257-210.1038/s41467-020-20257-2 ↗
26. 10.1038/s41928-020-00492-710.1038/s41928-020-00492-7 ↗
27. 10.1038/s41467-022-29364-810.1038/s41467-022-29364-8 ↗
28. 10.1002/adma.20220604210.1002/adma.202206042 ↗
29. 10.1002/aelm.20220037010.1002/aelm.202200370 ↗
30. 10.1063/5.012439010.1063/5.0124390 ↗
31. 10.1002/aelm.20180079510.1002/aelm.201800795 ↗
32. 10.1103/PhysRevApplied.19.01405410.1103/PhysRevApplied.19.014054 ↗
33. 10.1016/j.mtener.2021.10071110.1016/j.mtener.2021.100711 ↗
34. 10.1038/s41928-018-0103-310.1038/s41928-018-0103-3 ↗
35. 10.35848/1882-0786/ac75a910.35848/1882-0786/ac75a9 ↗
36. 10.1109/TED.2018.279793610.1109/TED.2018.2797936 ↗
37. 10.1109/LED.2022.320885510.1109/LED.2022.3208855 ↗
38. 10.1063/5.007308510.1063/5.0073085 ↗
39. 10.1063/1.479159810.1063/1.4791598 ↗
40. 10.1002/polb.2406810.1002/polb.24068 ↗
41. 10.1002/masy.20095050810.1002/masy.200950508 ↗
42. 10.1143/JJAP.39.548110.1143/JJAP.39.5481 ↗
43. 10.1038/srep0507510.1038/srep05075 ↗
44. 10.1063/1.271047710.1063/1.2710477 ↗
45. 10.1063/1.360731610.1063/1.3607316 ↗
46. Kandel, E. R.; Schwartz, J. H.; Jessell, T. M.; Siegelbaum, S.; Hudspeth, A. J.; Mack, S. Principles of Neural Science, 5th ed. McGraw-Hill: New York, 2000; pp 126–147.
47. 10.1038/srep3629110.1038/srep36291 ↗
48. 10.1109/14.15579510.1109/14.155795 ↗
49. 10.1038/sj.npp.130155910.1038/sj.npp.1301559 ↗
50. 10.1021/acsami.0c1638510.1021/acsami.0c16385 ↗
51. 10.1109/LED.2018.282433910.1109/LED.2018.2824339 ↗
52. 10.1088/1361-6528/ac368710.1088/1361-6528/ac3687 ↗
53. 10.1016/j.neuron.2008.10.01110.1016/j.neuron.2008.10.011 ↗
54. 10.1073/pnas.040265010110.1073/pnas.0402650101 ↗
55. 10.1063/1.356203410.1063/1.3562034 ↗
56. 10.1088/0022-3727/49/37/37510510.1088/0022-3727/49/37/375105 ↗
57. 10.1002/aelm.20220013710.1002/aelm.202200137 ↗
58. 10.1088/0957-4484/27/36/36520410.1088/0957-4484/27/36/365204 ↗
59. 10.1038/s41586-021-04223-610.1038/s41586-021-04223-6 ↗
60. 10.1002/adma.20200420710.1002/adma.202004207 ↗
61. 10.1002/adfm.20220236610.1002/adfm.202202366 ↗
62. 10.1021/nl200221z10.1021/nl200221z ↗
63. 10.1007/s40820-022-00978-310.1007/s40820-022-00978-3 ↗
64. 10.1103/PhysRevLett.115.26760110.1103/PhysRevLett.115.267601 ↗
65. 10.1016/j.nanoen.2020.10501410.1016/j.nanoen.2020.105014 ↗