Artificial synapses with a sponge-like double-layer porous oxide memristor
Source abstract
Abstract
Abstract Closely following the rapid development of artificial intelligence, studies of the human brain and neurobiology are focusing on the biological mechanisms of neurons and synapses. Herein, a memory system employing a nanoporous double-layer structure for simulation of synaptic functions is described. The sponge-like double-layer porous (SLDLP) oxide stack of Pt/porous LiCoO2/porous SiO2/Si is designed as presynaptic and postsynaptic membranes. This bionic structure exhibits high ON–OFF ratios up to 108 during the stability test, and data can be maintained for 105 s despite a small read voltage of 0.5 V. Typical synaptic functions, such as nonlinear transmission characteristics, spike-timing-dependent plasticity, and learning-experience behaviors, are achieved simultaneously with this device. Based on the hydrodynamic transport mechanism of water molecules in porous sponges and the principle of water storage, the synaptic behavior of the device is discussed. The SLDLP oxide memristor is very promising due to its excellent synaptic performance and potential in neuromorphic computing.
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Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (65)
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1. Zhu, J. D., Zhang, T., Yang, Y. C. & Huang, R. A comprehensive review on emerging artificial neuromorphic devices. Appl. Phys. Rev. 7, 011312 (2020).10.1063/1.5118217 ↗
2. Yu, F. et al. Artificial tactile perceptual neuron with nociceptive and pressure decoding abilities. ACS Appl. Mater. Interfaces 12, 26258–26266 (2020).10.1021/acsami.0c04718 ↗
3. Zhu, L. Q., Wan, C. J., Guo, L. Q., Shi, Y. & Wan, Q. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 5, 315 (2014).10.1038/ncomms4158 ↗
4. Zhang, X. Z., Huang, A. P., Hu, Q., Xiao, Z. S. & Chu, P. K. Neuromorphic computing with memristor crossbar. Phys. Status Solidi A, 215, 1700875 (2018).10.1002/pssa.201700875 ↗
5. Tang, J. et al. Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31, 1902761 (2019).10.1002/adma.201902761 ↗
6. La, TorreC., Zurhelle, A. F., Breuer, T., Waser, R. & Menzel, S. Compact modeling of complementary switching in oxide-based ReRAM devices. IEEE Trans. Electron. Devices 66, 1268–1275 (2019).10.1109/TED.2019.2892997 ↗
7. Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).10.1093/mind/LIX.236.433 ↗
8. Hebb, D. O. The Organization Of Behavior: a Neuropsychological Theory (Psychology Press, 2005).10.4324/9781410612403 ↗
9. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).10.1038/s41928-017-0006-8 ↗
10. Sangwan, V. K. & Hersam, M. C. Neuromorphic nanoelectronic materials. Nat. Nanotechnol., 15, 517–528 (2020).10.1038/s41565-020-0647-z ↗
11. Gu, L. L. et al. A biomimetic eye with a hemispherical perovskite nanowire array retina. Nature 581, 278–282 (2020).10.1038/s41586-020-2285-x ↗
12. Yin, J. et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv. Funct. Mater. 28, 1706927 (2018).10.1002/adfm.201706927 ↗
13. Li, D. et al. MoS2 memristors exhibiting variable switching characteristics toward biorealistic synaptic emulation. ACS Nano 12, 9240–9252 (2018).10.1021/acsnano.8b03977 ↗
14. Ge, S. P. et al. Low‐dimensional lead‐free inorganic perovskites for resistive switching with ultralow bias. Adv. Funct. Mater. 30, 2002110 (2020).10.1002/adfm.202002110 ↗
15. Guan, X. et al. A monolithic artificial iconic memory based on highly stable perovskite-metal multilayers. Appl. Phys. Rev. 7, 031401 (2020).10.1063/5.0009713 ↗
16. Cao, W. et al. 2-D layered materials for next-generation electronics: opportunities and challenges. IEEE Trans. Electron. Devices 65, 4109–4121 (2018).10.1109/TED.2018.2867441 ↗
17. Zhu, J. et al. Ion gated synaptic transistors based on 2D van der Waals crystals with tunable diffusive dynamics. Adv. Mater. 30, 1800195 (2018).10.1002/adma.201800195 ↗
18. Kim, S. et al. Scaling effect on silicon nitride memristor with highly doped Si substrate. Small 14, 1704062 (2018).10.1002/smll.201704062 ↗
19. Mazur, T., Zawal, P. & Szaciłowski, K. Synaptic plasticity, metaplasticity and memory effects in hybrid organic–inorganic bismuth-based materials. Nanoscale 11, 1080–1090 (2019).10.1039/C8NR09413F ↗
20. Shaarawy, N. et al. Design and analysis of 2T2M hybrid CMOS-memristor based RRAM. Microelectron. J. 73, 75–85 (2018).10.1016/j.mejo.2018.01.001 ↗
21. Xia, Q. et al. Memristor-CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett. 9, 3640–3645 (2009).10.1021/nl901874j ↗
22. He, X. et al. Memristive properties of hexagonal WO3 nanowires induced by oxygen vacancy migration. Nanoscale Res. Lett. 8, 50 (2013).10.1186/1556-276X-8-50 ↗
23. Nandakumar, S. R., Minvielle, M., Nagar, S., Dubourdieu, C. & Rajendran, B. A 250 mv Cu/SiO2/W memristor with half-integer quantum conductance states. Nano Lett. 16, 1602–1608 (2016).10.1021/acs.nanolett.5b04296 ↗
24. Paris, A. & Taioli, S. Multiscale investigation of oxygen vacancies in TiO2 anatase and their tole in memristor’s behavior. J. Phys. Chem. C 120, 22045–22053 (2016).10.1021/acs.jpcc.6b07196 ↗
25. Liu, D., Cheng, H., Zhu, X., Wang, G. & Wang, N. Analog memristors based on thickening/thinning of Ag nanofilaments in amorphous manganite thin films. ACS Appl. Mater. Interfaces 5, 11258–11264 (2013).10.1021/am403497y ↗
26. Wang, Y. et al. Resistive switching mechanism in silicon highly rich SiOx (x< 0.75) films based on silicon dangling bonds percolation model. Appl. Phys. Lett. 102, 042103 (2013).10.1063/1.4776695 ↗
27. Yao, J., Sun, Z., Zhong, L., Natelson, D. & Tour, J. M. Resistive switches and memories from silicon oxide. Nano Lett. 10, 4105–4110 (2010).10.1021/nl102255r ↗
28. Choi, B. J. et al. Electrical performance and scalability of Pt dispersed SiO2 nanometallic resistance switch. Nano Lett. 13, 3213–3217 (2013).10.1021/nl401283q ↗
29. Kwon, S. et al. Controllable switching filaments prepared via tunable and well-defined single truncated conical nanopore structures for fast and scalable SiOx memory. Nano Lett. 17, 7462–7470 (2017).10.1021/acs.nanolett.7b03373 ↗
30. Wang, G. et al. Nanoporous silicon oxide memory. Nano Lett. 14, 4694–4699 (2014).10.1021/nl501803s ↗
31. Wang, G. et al. Three-dimensional networked nanoporous Ta2O5-x memory system for ultrahigh density storage. Nano Lett. 15, 6009–6014 (2015).10.1021/acs.nanolett.5b02190 ↗
32. Kwon, S. et al. Structurally engineered nanoporous Ta2O5–x selector-less memristor for high uniformity and low power consumption. ACS Appl. Mater. Interfaces 9, 34015–34023 (2017).10.1021/acsami.7b06918 ↗
33. Wang, T. Y. et al. Three-dimensional nanoscale flexible memristor networks with ultralow power for information transmission and processing application. Nano Lett. 20, 4111–4120 (2020).10.1021/acs.nanolett.9b05271 ↗
34. Yan, D. et al. Soft three-dimensional network materials with rational bio-mimetic designs. Nat. Commun. 11, 1–11 (2020).10.1038/s41467-020-14996-5 ↗
35. Gao, Q. et al. Stability and repeatability of a karst-like hierarchical porous silicon oxide-based memristor. ACS Appl. Mater. Interfaces 11, 21734–21740 (2019).10.1021/acsami.9b06855 ↗
36. Mai, V. H. et al. Memristive and neuromorphic behavior in a LixCoO2 nanobattery. Sci. Rep. 5, 7761 (2015).10.1038/srep07761 ↗
37. Choi, Y. et al. Structural engineering of Li-based electronic synapse for high reliability. IEEE Electron. Device Lett. 40, 1992–1995 (2019).10.1109/LED.2019.2950202 ↗
38. Donders, M. E. et al. Atomic layer deposition of LiCoO2 thin-film electrodes for all-solid-state Li-ion micro-batteries. J. Electrochem. Soc. 160, A3066 (2013).10.1149/2.011305jes ↗
39. Xie, J. et al. Engineering the surface of LiCoO2 electrodes using atomic layer deposition for stable high-voltage lithium ion batteries. Nano Res. 10, 3754–3764 (2017).10.1007/s12274-017-1588-1 ↗
40. Nguyen, V. S. et al. Direct evidence of lithium ion migration in resistive switching of lithium cobalt oxide nanobatteries. Small 14, 1801038 (2018).10.1002/smll.201801038 ↗
41. Bezza, I. et al. LiCoO2 with double porous structure obtained by electrospray deposition and its evaluation as an electrode for lithium-ion batteries. J. Alloy. Comp. 805, 19–25 (2019).10.1016/j.jallcom.2019.07.062 ↗
42. Choi, Y. M. & Pyun, S. I. Effects of intercalation-induced stress on lithium transport through porous LiCoO2 electrode. Solid State Ion. 99, 173–183 (1997).10.1016/S0167-2738(97)00253-1 ↗
43. Xue, L., Savilov, S. V., Lunin, V. V. & Xia, H. Self‐standing porous LiCoO2 nanosheet arrays as 3D cathodes for flexible Li‐ion batteries. Adv. Funct. Mater. 28, 1705836 (2018).10.1002/adfm.201705836 ↗
44. Chang, W. S. et al. Quartz (SiO2): a new energy storage anode material for Li-ion batteries. Energy Environ. Sci. 5, 6895–6899 (2012).10.1039/c2ee00003b ↗
45. Ariel, N., Ceder, G., Sadoway, D. R. & Fitzgerald, E. A. Electrochemically controlled transport of lithium through ultrathin SiO2. J. Appl. Phys. 98, 023516 (2005).10.1063/1.1989431 ↗
46. Mu, G. et al. Microsphere‐like SiO2/MXene hybrid material enabling high performance anode for lithium ion batteries. Small 16, 1905430 (2020).10.1002/smll.201905430 ↗
47. Mehonic, A. et al. Silicon oxide (SiOx): a promising material for resistance switching? Adv. Mater. 30, 1801187 (2018).10.1002/adma.201801187 ↗
48. Hu, Q. et al. Lithium ion trapping mechanism of SiO2 in LiCoO2 based memristor. Sci. Rep. 9, 5081 (2019).10.1038/s41598-019-41508-3 ↗
49. Zhou, J. Resistive switching characteristics of PECVD-deposited porous SiO2-based electrochemical metallisation memory cells. Electron. Lett. 52, 965–966 (2016).10.1049/el.2015.4058 ↗
50. Tsai, T. M. et al. Performance and characteristics of double layer porous silicon oxide resistance random access memory. Appl. Phys. Lett. 102, 253509 (2013).10.1063/1.4812474 ↗
51. Shi, C. et al. Silk flexible electronics: from Bombyx mori Silk Ag nanoclusters hybrid materials to mesoscopic memristors and synaptic emulators. Adv. Funct. Mater. 29, 1904777 (2019).10.1002/adfm.201904777 ↗
52. Guo, Z. et al. Spontaneous preparation of hierarchically porous silica monoliths with uniform spherical mesopores confined in a well-defined macroporous framework. Dalton Trans. 44, 13592–13601 (2015).10.1039/C5DT01672J ↗
53. Feng, J. et al. Self‐aligned anisotropic plasmonic nanostructures. Adv. Mater. 31, 1900789 (2019).10.1002/adma.201900789 ↗
54. Xia, Y., Xiong, Y., Lim, B. & Skrabalak, S. E. Shape‐controlled synthesis of metal nanocrystals: simple chemistry meets complex physics? Angew. Chem. Int. Ed. 48, 60–103 (2009).10.1002/anie.200802248 ↗
55. Household sponge, mailto: https://m.quanjing.com/imgbuy/QJ9113240495.html.
56. Erokhin, V. et al. Material memristive device circuits with synaptic plasticity: learning and memory. BioNanoScience 1, 24–30 (2011).10.1007/s12668-011-0004-7 ↗
57. Chen, L., Li, C., Huang, T., Ahmad, H. G. & Chen, Y. A phenomenological memristor model for short-term/long-term memory. Phys. Lett. A 378, 2924–2930 (2014).10.1016/j.physleta.2014.08.018 ↗
58. Amsalem, O. et al. An efficient analytical reduction of detailed nonlinear neuron models. Nat. Commun. 11, 1–13 (2020).10.1038/s41467-019-13932-6 ↗
59. Ménétrier, M., Saadoune, I., Levasseur, S. & Delmas, C. The insulator-metal transition upon lithium deintercalation from LiCoO2: electronic properties and 7Li NMR study. J. Mater. Chem. 9, 1135–1140 (1999).10.1039/a900016j ↗
60. Milewska, A. et al. The nature of the nonmetal–metal transition in LixCoO2 oxide. Solid State Ion. 263, 110–118 (2014).10.1016/j.ssi.2014.05.011 ↗
61. Greenlee, J. D., Petersburg, C. F., Daly, W. G., Alamgir, F. M. & Alan Doolittle, W. In situ investigation of the channel conductance of a Li1-xCoO2 (0 < x < 0.5) ionic-electronic transistor. Appl. Phys. Lett. 102, 213502 (2013).10.1063/1.4807424 ↗
62. Yu, Y. et al. Synthesis of donor–acceptor gridarenes with tunable electronic structures for synaptic learning memristor. ACS Omega 4, 5863–5869 (2019).10.1021/acsomega.9b00172 ↗
63. Murre, J. M. & Dros, J. Replication and analysis of Ebbinghaus’ forgetting curve. PloS ONE 10, e0120644 (2015).10.1371/journal.pone.0120644 ↗
64. Linares-Barranco, B. et al. On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front. Neurosci. 5, 26 (2011).
65. Wang, Y. F., Lin, Y. C., Wang, I. T., Lin, T. P. & Hou, T. H. Characterization and modeling of nonfilamentary Ta/TaOx/TiO2/Ti analog synaptic device. Sci. Rep. 5, 10150 (2015).10.1038/srep10150 ↗