Ferroelectrics‐Electret Synergetic Organic Artificial Synapses with Single‐Polarity Driven Dynamic Reconfigurable Modulation
Source abstract
Abstract
Abstract Most current studies of artificial synapses only mimic the static plasticity, which is far from achieving the complex behaviors of the human brain. The few reported dynamic reconfigurable synapses based on ambipolar transistors switch the operating states by voltages with opposite polarity, which impedes the development of highly efficient synaptic readout circuits. To improve the efficiency, flexibility, and biocompatibility of dynamic reconfigurable synapses, here a ferroelectrics‐electret synergetic organic synaptic p‐type transistor (FESOST) is devised. Owing to the synergetic action of ferroelectric polarization switching and charge capture, FESOST exhibits single‐polarity driven dynamic reconfigurable operating states with different synaptic behaviors (potentiation and depression) in response to the same gate pulse in different modes (excitatory and inhibitory). In addition, various single‐polarity driven synaptic behaviors including short‐term/long‐term plasticity, paired‐pulse facilitation/depression, spike‐rate‐dependent plasticity, and spike‐number‐dependent plasticity are also simulated. Finally, the reconfigurable artificial temperature perception system is simulated for the complex emotions of humans in response to different weather stimuli for people of different constitutions. The novel device architecture represents a major step forward in the development of dynamic, reconfigurable, high‐efficiency, organic synapses.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Conducting polymers and applications · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Physics Science Qingdao University Qingdao 266071 P. R. China
College of Electronic and Information Engineering Qingdao University Qingdao 266071 P. R. China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Shandong Province
References (57)
Show source-provided references
1. 10.1038/nrn214810.1038/nrn2148 ↗
2. 10.1038/nature0566210.1038/nature05662 ↗
3. 10.1021/acsnano.1c0467610.1021/acsnano.1c04676 ↗
4. 10.1016/S0301-0082(99)00070-210.1016/S0301-0082(99)00070-2 ↗
5. 10.1002/adma.20180129110.1002/adma.201801291 ↗
6. 10.1021/acsami.2c1983610.1021/acsami.2c19836 ↗
7. 10.1002/adfm.20220236610.1002/adfm.202202366 ↗
8. 10.1109/TNN.2005.86085010.1109/TNN.2005.860850 ↗
9. 10.1126/science.125464210.1126/science.1254642 ↗
10. 10.3389/fnins.2015.0014110.3389/fnins.2015.00141 ↗
11. 10.1038/s41928-020-0433-910.1038/s41928-020-0433-9 ↗
12. Chen B. · Adv. Electronic Mater. · 2022 · 8
13. 10.1021/acsnano.2c0103510.1021/acsnano.2c01035 ↗
14. 10.1002/adfm.20220990710.1002/adfm.202209907 ↗
15. 10.1021/acsnano.1c0556510.1021/acsnano.1c05565 ↗
16. 10.1002/adma.20200361010.1002/adma.202003610 ↗
17. 10.1021/nn304724q10.1021/nn304724q ↗
18. 10.1016/j.tins.2014.04.00210.1016/j.tins.2014.04.002 ↗
19. 10.1146/annurev-neuro-071714-03400210.1146/annurev-neuro-071714-034002 ↗
20. 10.1038/nrn92010.1038/nrn920 ↗
21. 10.1038/nn.382310.1038/nn.3823 ↗
22. 10.1038/nrn235610.1038/nrn2356 ↗
23. 10.1038/ncomms928110.1038/ncomms9281 ↗
24. 10.1038/s41583-021-00523-y10.1038/s41583-021-00523-y ↗
25. 10.3389/fncir.2013.0003710.3389/fncir.2013.00037 ↗
26. 10.1021/acsnano.6b0629310.1021/acsnano.6b06293 ↗
27. 10.1038/s41699-019-0114-610.1038/s41699-019-0114-6 ↗
28. 10.1021/acsami.1c2432710.1021/acsami.1c24327 ↗
29. 10.1021/acs.nanolett.5b0328310.1021/acs.nanolett.5b03283 ↗
30. 10.1002/adfm.20210191710.1002/adfm.202101917 ↗
31. 10.1002/adma.20210775410.1002/adma.202107754 ↗
32. Wang H. · Adv. Mater. · 2022 · 34
33. 10.1002/adma.20220205910.1002/adma.202202059 ↗
34. 10.1038/s41563-018-0047-z10.1038/s41563-018-0047-z ↗
35. 10.1038/nmat485610.1038/nmat4856 ↗
36. 10.1063/1.271385610.1063/1.2713856 ↗
37. 10.3390/ma1424763510.3390/ma14247635 ↗
38. 10.1002/adfm.20070101910.1002/adfm.200701019 ↗
39. 10.1021/acsami.0c0116210.1021/acsami.0c01162 ↗
40. 10.1002/aelm.20220098410.1002/aelm.202200984 ↗
41. 10.1002/aelm.20210134210.1002/aelm.202101342 ↗
42. 10.1021/acsami.0c1960310.1021/acsami.0c19603 ↗
43. 10.1063/5.007715310.1063/5.0077153 ↗
44. 10.1021/acs.nanolett.9b0018010.1021/acs.nanolett.9b00180 ↗
45. 10.1523/JNEUROSCI.0439-05.200510.1523/JNEUROSCI.0439-05.2005 ↗
46. 10.1002/adfm.20080037810.1002/adfm.200800378 ↗
47. 10.1039/D2TC01132H10.1039/D2TC01132H ↗
48. 10.1038/nmat305410.1038/nmat3054 ↗
49. 10.1021/acsnano.7b0827210.1021/acsnano.7b08272 ↗
50. 10.1016/j.nanoen.2020.10525810.1016/j.nanoen.2020.105258 ↗
51. 10.1002/adfm.20211305310.1002/adfm.202113053 ↗
52. 10.1021/acsnano.7b0833110.1021/acsnano.7b08331 ↗
53. 10.1002/adfm.20210797310.1002/adfm.202107973 ↗
54. 10.1039/C8NR07133K10.1039/C8NR07133K ↗
55. 10.1039/D1MH02036F10.1039/D1MH02036F ↗
56. 10.1152/physrev.00047.202110.1152/physrev.00047.2021 ↗
57. 10.1152/physrev.00009.200510.1152/physrev.00009.2005 ↗