journal article·2023-05-31

Reconfigurable Low-Power TiO2 Memristor for Integration of Artificial Synapse and Nociceptor

Mousam Charan Sahu, Anjan Kumar Jena, Sameer Kumar Mallik, Suman Roy, Sandhyarani Sahoo, R. S. Ajimsha, Pankaj Misra, Satyaprakash Sahoo

ACS Applied Materials & Interfaces· Volume 15· Issue 21
Publisher page

Abstract

Bio-mimetic advanced electronic systems are emerging rapidly, engrossing their applications in neuromorphic computing, humanoid robotics, tactile sensors, and so forth. The biological synaptic and nociceptive functions are governed by intricate neurotransmitter dynamics that involve both short-term and long-term plasticity. To emulate the neuronal dynamics in an electronic device, an Ag/TiO 2 /Pt/SiO 2 /Si memristor is fabricated, exhibiting compliance current controlled reversible transition of volatile switching (VS) and non-volatile switching (NVS). The origin of the VS and NVS depends on the diameter of the conducting filament, which is explained using a field-induced nucleation theory and validated by temporal current response measurements. The switching delay of the device is used to determine the characteristic nociceptive behaviors such as threshold, relaxation, inadaptation, allodynia, and hyperalgesia. The short-term and long-term retention loss attributed to the VS and NVS, respectively, is used to emulate short-term memory and long-term memory of the biological brain in a single device. More importantly, synergistically modulating the VS–NVS transition, the complex spike rate-dependent (SRDP) and spike time-dependent plasticity (STDP) with a weight change of up to 600% is demonstrated in the same device, which is the highest reported so far for TiO 2 memristors. Furthermore, the device exhibits very low power consumption, ∼3.76 pJ/spike, and can imitate synaptic and nociceptive functions. The consolidation of complex nociceptive and synaptic behavior in a single memristor facilitates low-power integration of scalable intelligent sensors and neuromorphic devices.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neural dynamics and brain function

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

Affiliations

Laboratory for Low Dimensional Materials, Institute of Physics, Bhubaneswar 751005, India

Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India

Oxide Nano Electronics Lab, Laser Material Processing Division, Raja Ramanna Centre for Advanced Technology, Indore 452013, India

References (61)

Show source-provided references
1. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
2. Horowitz, M. 1.1 Computing’s Energy Problem (and What We Can Do about It). 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2014; pp 10–14.10.1109/ISSCC.2014.6757323 ↗
3. McKee, S. A. Reflections on the Memory Wall. Proceedings of the 1st Conference on Computing Frontiers; CF ’04; Association for Computing Machinery: New York, NY, USA, 2004; p 162.10.1145/977091.977115 ↗
4. 10.1038/s42254-020-0208-210.1038/s42254-020-0208-2 ↗
5. 10.1038/s41928-019-0360-910.1038/s41928-019-0360-9 ↗
6. 10.1016/j.mtphys.2022.10065010.1016/j.mtphys.2022.100650 ↗
7. 10.1016/j.mtphys.2021.10039310.1016/j.mtphys.2021.100393 ↗
8. 10.1002/admt.20220112510.1002/admt.202201125 ↗
9. 10.1038/nrn230310.1038/nrn2303 ↗
10. 10.1038/s41578-022-00434-z10.1038/s41578-022-00434-z ↗
11. 10.1038/nmat475610.1038/nmat4756 ↗
12. 10.1038/s41928-019-0270-x10.1038/s41928-019-0270-x ↗
13. 10.1002/adma.20170591410.1002/adma.201705914 ↗
14. 10.1016/j.mtadv.2020.10012510.1016/j.mtadv.2020.100125 ↗
15. 10.1038/nnano.2017.8310.1038/nnano.2017.83 ↗
16. 10.1038/s41928-017-0002-z10.1038/s41928-017-0002-z ↗
17. 10.1021/acsami.2c1722810.1021/acsami.2c17228 ↗
18. 10.1016/j.mattod.2022.08.01710.1016/j.mattod.2022.08.017 ↗
19. 10.1038/s41467-017-00869-x10.1038/s41467-017-00869-x ↗
20. 10.1002/adfm.20170486210.1002/adfm.201704862 ↗
21. 10.1038/s41467-017-02572-310.1038/s41467-017-02572-3 ↗
22. 10.1109/LED.2017.278275210.1109/LED.2017.2782752 ↗
23. 10.1016/j.mtadv.2021.10019210.1016/j.mtadv.2021.100192 ↗
24. 10.1039/D1TC04201G10.1039/D1TC04201G ↗
25. 10.1016/j.neuron.2007.07.01610.1016/j.neuron.2007.07.016 ↗
26. 10.1109/JSEN.2011.215983510.1109/JSEN.2011.2159835 ↗
27. 10.1126/scirobotics.abc813410.1126/scirobotics.abc8134 ↗
28. 10.1038/s41528-022-00183-710.1038/s41528-022-00183-7 ↗
29. 10.1126/science.abd364310.1126/science.abd3643 ↗
30. 10.1016/j.mtphys.2021.10054010.1016/j.mtphys.2021.100540 ↗
31. 10.1002/adfm.20210109910.1002/adfm.202101099 ↗
32. 10.1002/adma.20200785110.1002/adma.202007851 ↗
33. 10.1109/LED.2015.243033210.1109/LED.2015.2430332 ↗
34. 10.1021/acs.jpclett.1c0012110.1021/acs.jpclett.1c00121 ↗
35. 10.1109/TED.2021.307602910.1109/TED.2021.3076029 ↗
36. 10.1038/nature0693210.1038/nature06932 ↗
37. 10.3390/electronics403058610.3390/electronics4030586 ↗
38. 10.1109/TED.2019.292889010.1109/TED.2019.2928890 ↗
39. 10.1109/TED.2019.292888810.1109/TED.2019.2928888 ↗
40. 10.1063/1.498516510.1063/1.4985165 ↗
41. 10.1063/1.271502410.1063/1.2715024 ↗
42. 10.1002/adfm.20211124210.1002/adfm.202111242 ↗
43. 10.1002/adma.20170432010.1002/adma.201704320 ↗
44. 10.1021/nn202983n10.1021/nn202983n ↗
45. 10.1038/s41467-018-07979-010.1038/s41467-018-07979-0 ↗
46. 10.3758/BF0321131610.3758/BF03211316 ↗
47. 10.1002/pssr.20200035710.1002/pssr.202000357 ↗
48. 10.1038/s42256-018-0001-410.1038/s42256-018-0001-4 ↗
49. 10.1063/5.012820010.1063/5.0128200 ↗
50. Atkinson R. C. · 1968 · 2 · 89
51. 10.1038/nature0301010.1038/nature03010 ↗
52. 10.1021/acs.nanolett.5b0069710.1021/acs.nanolett.5b00697 ↗
53. 10.1002/smtd.20210056610.1002/smtd.202100566 ↗
54. 10.1002/admt.20210037310.1002/admt.202100373 ↗
55. 10.1109/TED.2023.323658810.1109/TED.2023.3236588 ↗
56. 10.1002/adma.20180144710.1002/adma.201801447 ↗
57. 10.1063/5.009808710.1063/5.0098087 ↗
58. 10.1002/adfm.20180402510.1002/adfm.201804025 ↗
59. 10.1039/C9NR08872E10.1039/C9NR08872E ↗
60. 10.1021/acsami.2c0366710.1021/acsami.2c03667 ↗
61. 10.1002/smll.20220018510.1002/smll.202200185 ↗