Fully Printed All-Solid-State Organic Flexible Artificial Synapse for Neuromorphic Computing
Source abstract
Abstract
Nonvolatile, flexible artificial synapses that can be used for brain-inspired computing are highly desirable for emerging applications such as human-machine interfaces, soft robotics, medical implants, and biological studies. Printed devices based on organic materials are very promising for these applications due to their sensitivity to ion injection, intrinsic printability, biocompatibility, and great potential for flexible/stretchable electronics. Herein, we report the experimental realization of a nonvolatile artificial synapse using organic polymers in a scalable fabrication process. The three-terminal electrochemical neuromorphic device successfully emulates the key features of biological synapses: long-term potentiation/depression, spike timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow energy consumption. The artificial synapse network exhibits an excellent endurance against bending tests and enables a direct emulation of logic gates, which shows the feasibility of using them in futuristic hierarchical neural networks. Based on our demonstration of 100 distinct, nonvolatile conductance states, we achieved a high accuracy in pattern recognition and face classification neural network simulations.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Conducting polymers and applications · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. 10.1126/science.125464210.1126/science.1254642 ↗
2. 10.1038/nature1696110.1038/nature16961 ↗
3. 10.1145/359576.35957910.1145/359576.359579 ↗
4. 10.1038/nature0693210.1038/nature06932 ↗
5. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
6. 10.1088/0957-4484/24/38/38200110.1088/0957-4484/24/38/382001 ↗
7. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
8. 10.1038/nature2299410.1038/nature22994 ↗
9. 10.1038/nnano.2017.8310.1038/nnano.2017.83 ↗
10. 10.1021/nn405827t10.1021/nn405827t ↗
11. 10.1021/acsnano.7b0731710.1021/acsnano.7b07317 ↗
12. 10.1038/s41928-018-0023-210.1038/s41928-018-0023-2 ↗
13. 10.1038/s41928-017-0002-z10.1038/s41928-017-0002-z ↗
14. 10.3389/fnins.2014.0020510.3389/fnins.2014.00205 ↗
15. 10.1109/JPROC.2010.207005010.1109/JPROC.2010.2070050 ↗
16. 10.1038/nnano.2016.7010.1038/nnano.2016.70 ↗
17. 10.1038/ncomms415810.1038/ncomms4158 ↗
18. 10.1038/nature2574710.1038/nature25747 ↗
19. 10.1038/nmat382310.1038/nmat3823 ↗
20. 10.1021/acs.nanolett.6b0401010.1021/acs.nanolett.6b04010 ↗
21. 10.1021/acs.nanolett.6b0330010.1021/acs.nanolett.6b03300 ↗
22. 10.1038/nmat341510.1038/nmat3415 ↗
23. 10.1038/ncomms1473610.1038/ncomms14736 ↗
24. 10.1080/00018732.2010.54496110.1080/00018732.2010.544961 ↗
25. 10.1109/JPROC.2012.219036910.1109/JPROC.2012.2190369 ↗
26. 10.1038/nmat485610.1038/nmat4856 ↗
27. 10.1016/j.orgel.2011.12.01810.1016/j.orgel.2011.12.018 ↗
28. 10.1038/s41928-018-0103-310.1038/s41928-018-0103-3 ↗
29. 10.1002/adma.20150367410.1002/adma.201503674 ↗
30. 10.1038/ncomms1544810.1038/ncomms15448 ↗
31. 10.1038/s41467-017-00803-110.1038/s41467-017-00803-1 ↗
32. 10.1021/acsami.8b1209210.1021/acsami.8b12092 ↗
33. 10.1021/acsami.8b0744310.1021/acsami.8b07443 ↗
34. 10.1021/acsnano.6b0536810.1021/acsnano.6b05368 ↗
35. 10.1038/ncomms514710.1038/ncomms5147 ↗
36. 10.1038/361031a010.1038/361031a0 ↗
37. 10.1038/8145310.1038/81453 ↗
38. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
39. NanGate, S. 45 nm Open Cell Library, 2008. http://www.nangate.com/.
40. 10.1002/adma.20150271910.1002/adma.201502719 ↗
41. Bache K. · 2013
42. 10.1109/MSP.2012.221147710.1109/MSP.2012.2211477 ↗
43. ORL Database of Faces; AT&T Laboratories: Cambridge, 1992–1994.
44. 10.1007/s00521-012-1129-510.1007/s00521-012-1129-5 ↗
45. 10.1021/acsnano.7b0682310.1021/acsnano.7b06823 ↗