Neuromorphic nanoelectronic materials
Source abstract
Abstract
Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Photoreceptor and optogenetics research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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1. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (The MIT Press, 2016).
2. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).10.1038/nature14539 ↗
3. James, C. D. et al. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. Biol. Insp. Cog. Arch. 19, 49–64 (2017).
4. Schuman, C. D. et al. A survey of neuromorphic computing and neural networks in hardware. Preprint at https://arxiv.org/abs/1705.06963 (2018).
5. Bear, M. F., Connors, B. W. & Paradiso, M. A. Neuroscience: Exploring the Brain (Wolters Kluwer, 2015).
6. Indiveri, G., Chicca, E. & Douglas, R. J. Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cogn. Comp. 1, 119–127 (2009).10.1007/s12559-008-9003-6 ↗
7. Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neuro. 5, 73 (2011).
8. Krestinskaya, O., James, A. P. & Leon, C. O. Neuro-memristive circuits for edge computing: A review. IEEE Trans. Neural Netw. Learn. Syst. 31, 4–23 (2020).10.1109/TNNLS.2019.2899262 ↗
9. Jeong, D. S. et al. Memristors for energy-efficient new computing paradigms. Adv. Elec. Mater. 2, 1600090 (2016).10.1002/aelm.201600090 ↗
10. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).10.1038/nnano.2012.240 ↗
11. Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).10.1126/science.1254642 ↗
12. Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).10.1109/JPROC.2014.2304638 ↗
13. Tuma, T. et al. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).10.1038/nnano.2016.70 ↗
14. Duygu, K., Shimeng, Y. & Wong, H. S. P. Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013).10.1088/0957-4484/24/38/382001 ↗
15. Wong, H. S. P. et al. Metal-oxide RRAM. Proc. IEEE 100, 1951–1970 (2012).10.1109/JPROC.2012.2190369 ↗
16. Yu, S. & Chen, P. Y. Emerging memory technologies: Recent trends and prospects. IEEE Sol. Stat. Circuit Mag. 8, 43–56 (2016).10.1109/MSSC.2016.2546199 ↗
17. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).10.1038/nature06932 ↗
18. Sengupta, A. & Roy, K. Neuromorphic computing enabled by physics of electron spins: Prospects and perspectives. Appl. Phys. Exp. 11, 030101 (2018).10.7567/APEX.11.030101 ↗
19. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).10.1038/nature14441 ↗
20. Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2, 89–124 (2017).
21. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Elect. 1, 22–29 (2018).10.1038/s41928-017-0006-8 ↗
22. Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018).10.1038/s41928-018-0023-2 ↗
23. Zidan, M. A. et al. A general memristor-based partial differential equation solver. Nat. Electron. 1, 411–420 (2018).10.1038/s41928-018-0100-6 ↗
24. Sun, Z. et al. Solving matrix equations in one step with cross-point resistive arrays. Proc. Natl Acad. Sci. USA 116, 4123–4128 (2019).10.1073/pnas.1815682116 ↗
25. Bayat, F. M. et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9, 2331 (2018).10.1038/s41467-018-04482-4 ↗
26. Prezioso, M. et al. Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits. Nat. Commun. 9, 5311 (2018).10.1038/s41467-018-07757-y ↗
27. Shulaker, M. M. et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature 547, 74–78 (2017).10.1038/nature22994 ↗
28. Jariwala, D. et al. Carbon nanomaterials for electronics, optoelectronics, photovoltaics, and sensing. Chem. Soc. Rev. 42, 2824–2860 (2013).10.1039/C2CS35335K ↗
29. Jariwala, D. et al. Emerging device applications for semiconducting two-dimensional transition metal dichalcogenides. ACS Nano 8, 1102–1120 (2014).10.1021/nn500064s ↗
30. Sangwan, V. K. & Hersam, M. C. Electronic transport in two-dimensional materials. Annu. Rev. Phys. Chem. 69, 299–325 (2018).10.1146/annurev-physchem-050317-021353 ↗
31. Jariwala, D., Marks, T. J. & Hersam, M. C. Mixed-dimensional van der Waals heterostructures. Nat. Mater. 16, 170–181 (2016).10.1038/nmat4703 ↗
32. Upadhyay, N. K. et al. Emerging memory devices for neuromorphic computing. Adv. Mater. Tech. 4, 1800589 (2019).10.1002/admt.201800589 ↗
33. Sangwan, V. K. et al. Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018).10.1038/nature25747 ↗
34. Sun, L. et al. Synaptic computation enabled by Joule heating of single-layered semiconductors for sound localization. Nano Lett. 18, 3229–3234 (2018).10.1021/acs.nanolett.8b00994 ↗
35. Zhu, X., Li, D., Liang, X. & Lu, W. D. Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18, 141–148 (2018).10.1038/s41563-018-0248-5 ↗
36. van de Burgt, Y. et al. Organic electronics for neuromorphic computing. Nat. Electron 1, 386–397 (2018).10.1038/s41928-018-0103-3 ↗
37. Rivnay, J. et al. Organic electrochemical transistors. Nat. Rev. Mater. 3, 17086 (2018).10.1038/natrevmats.2017.86 ↗
38. Sanchez Esqueda, I. et al. Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing. ACS Nano 12, 7352–7361 (2018).10.1021/acsnano.8b03831 ↗
39. Kim, S. et al. Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol. ACS Nano 11, 2814–2822 (2017).10.1021/acsnano.6b07894 ↗
40. 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 ↗
41. Wang, S. et al. A MoS2/PTCDA hybrid heterojunction synapse with efficient photoelectric dual modulation and versatility. Adv. Mater. 31, 1806227 (2018).10.1002/adma.201806227 ↗
42. Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).10.1126/science.aao0098 ↗
43. Ziegler, M., Wenger, C., Chicca, E. & Kohlstedt, H. Tutorial: Concepts for closely mimicking biological learning with memristive devices: Principles to emulate cellular forms of learning. J. Appl. Phys. 124, 152003 (2018).10.1063/1.5042040 ↗
44. McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115 (1943).10.1007/BF02478259 ↗
45. Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psych. Rev. 65, 386–408 (1958).10.1037/h0042519 ↗
46. Hertz, J., Krogh, A. & Palmer, R. G. Introduction to the Theory of Neural Computation (Westview Press, 1991).10.1063/1.2810360 ↗
47. Wasserman, P. D. Neural Computing: Theory and Practice (Van Nostrand Reinhold, 1965).
48. Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physio. 117, 500–544 (1952).10.1113/jphysiol.1952.sp004764 ↗
49. Leon, C. Memristor, Hodgkin–Huxley, and edge of chaos. Nanotechnology 24, 383001 (2013).10.1088/0957-4484/24/38/383001 ↗
50. Pershin, Y. V. & Di Ventra, M. Memory effects in complex materials and nanoscale systems. Adv. Phys. 60, 145–227 (2011).10.1080/00018732.2010.544961 ↗
51. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).10.1038/323533a0 ↗
52. Minsky, M. A Neural-Analogue Calculator Based Upon a Probability Model of Reinforcement (Harvard University Psychological Laboratories, 1952).
53. Diorio, C., Hasler, P., Minch, A. & Mead, C. A. A single-transistor silicon synapse. IEEE Trans. Electron. Dev. 43, 1972–1980 (1996).10.1109/16.543035 ↗
54. Buonomano, D. V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113 (2009).10.1038/nrn2558 ↗
55. Qian, C. et al. Multi-gate organic neuron transistors for spatiotemporal information processing. Appl. Phys. Lett. 110, 083302 (2017).10.1063/1.4977069 ↗
56. Lanza, M. et al. Recommended methods to study resistive switching devices. Adv. Elec. Mater. 5, 1800143 (2019).10.1002/aelm.201800143 ↗
57. Pi, S. et al. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nat. Nanotechnol. 14, 35–39 (2018).10.1038/s41565-018-0302-0 ↗
58. Seok, J. Y. et al. A review of three-dimensional resistive switching cross-bar array memories from the integration and materials property points of view. Adv. Funct. Mater. 24, 5316–5339 (2014).10.1002/adfm.201303520 ↗
59. Waser, R., Dittmann, R., Staikov, G. & Szot, K. Redox-based resistive switching memories – Nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21, 2632–2663 (2009).10.1002/adma.200900375 ↗
60. Tan, C., Liu, Z., Huang, W. & Zhang, H. Non-volatile resistive memory devices based on solution-processed ultrathin two-dimensional nanomaterials. Chem. Soc. Rev. 44, 2615–2628 (2015).10.1039/C4CS00399C ↗
61. Yang, J. J. et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3, 429–433 (2008).10.1038/nnano.2008.160 ↗
62. Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).10.1038/nmat3510 ↗
63. Chang, S. et al. Occurrence of both unipolar memory and threshold resistance switching in a NiO film. Phys. Rev. Lett. 102, 026801 (2009).10.1103/PhysRevLett.102.026801 ↗
64. Linn, E., Rosezin, R., Kugeler, C. & Waser, R. Complementary resistive switches for passive nanocrossbar memories. Nat. Mater. 9, 403–406 (2010).10.1038/nmat2748 ↗
65. Kumar, S., Strachan, J. P. & Williams, R. S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 548, 318–321 (2017).10.1038/nature23307 ↗
66. Wang, Z. et al. Capacitive neural network with neuro-transistors. Nat. Commun. 9, 3208 (2018).
67. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).10.1038/nmat4756 ↗
68. Chua, L. O. & Sung, Mo,K. Memristive devices and systems. Proc. IEEE 64, 209–223 (1976).10.1109/PROC.1976.10092 ↗
69. Abdelouahab, M.-S., Lozi, R. & Chua, L. Memfractance: A mathematical paradigm for circuit elements with memory. Intern. J. Bifur. Chaos Appl. Sci. Engrg. 24, 1430023 (2014).10.1142/S0218127414300237 ↗
70. Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).10.1021/acs.nanolett.5b00697 ↗
71. Xia, Q. et al. Two- and three-terminal resistive switches: Nanometer-scale memristors and memistors. Adv. Funct. Mater. 21, 2660–2665 (2011).10.1002/adfm.201100180 ↗
72. Yang, Y., Chen, B. & Lu, W. D. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Adv. Mater. 27, 7720–7727 (2015).10.1002/adma.201503202 ↗
73. Schultz, W. Predictive reward signal of dopamine neurons. J. Neurophysio. 80, 1–27 (1998).10.1152/jn.1998.80.1.1 ↗
74. Sangwan, V. K. et al. Gate-tunable memristive phenomena mediated by grain boundaries in single-layer MoS2. Nat. Nanotechnol. 10, 403–406 (2015).10.1038/nnano.2015.56 ↗
75. Yang, Y. et al. Three-terminal memtransistors based on two-dimensional layered gallium selenide nanosheets for potential low-power electronics applications. Nano Energy 57, 566–573 (2019).10.1016/j.nanoen.2018.12.057 ↗
76. Wang, L. et al. Artificial synapses based on multiterminal memtransistors for neuromorphic application. Adv. Funct. Mater. 29, 1901106 (2019).10.1002/adfm.201901106 ↗
77. Mouttet, B. Memristive systems analysis of 3-terminal devices. In Proc. 2010 17th IEEE International Conference on Electronics, Circuits and Systems 930–933 (IEEE, 2010).10.1109/ICECS.2010.5724665 ↗
78. Caporale, N. & Dan, Y. Spike timing–dependent plasticity: A Hebbian learning rule. Ann. Rev. Neuro. 31, 25–46 (2008).10.1146/annurev.neuro.31.060407.125639 ↗
79. Bi, G.-q & Poo, M.-m Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neuro. 18, 10464–10472 (1998).10.1523/JNEUROSCI.18-24-10464.1998 ↗
80. Sun, C. et al. Single-chip microprocessor that communicates directly using light. Nature 528, 534–538 (2015).10.1038/nature16454 ↗
81. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).10.1126/science.aat8084 ↗
82. Mesaritakis, C., Kapsalis, A., Bogris, A. & Syvridis, D. Artificial neuron based on integrated semiconductor quantum dot mode-locked lasers. Sci. Rep. 6, 39317 (2016).10.1038/srep39317 ↗
83. Maier, P. et al. Electro-photo-sensitive memristor for neuromorphic and arithmetic computing. Phys. Rev. Appl. 5, 054011 (2016).10.1103/PhysRevApplied.5.054011 ↗
84. Maier, P. et al. Memristive operation mode of a site-controlled quantum dot floating gate transistor. Appl. Phys. Lett. 106, 203501 (2015).10.1063/1.4921061 ↗
85. Wang, Y. et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30, 1802883 (2018).10.1002/adma.201802883 ↗
86. Han, S.-T. et al. Black phosphorus quantum dots with tunable memory properties and multilevel resistive switching characteristics. Adv. Sci. 4, 1600435 (2017).10.1002/advs.201600435 ↗
87. Alibart, F. et al. An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater. 20, 330–337 (2010).10.1002/adfm.200901335 ↗
88. Goswami, S. et al. Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nat. Mater. 16, 1216–1224 (2017).10.1038/nmat5009 ↗
89. Chua, L. O. Nonlinear circuit foundations for nanodevices. I. The four-element torus. Proc. IEEE 91, 1830–1859 (2003).10.1109/JPROC.2003.818319 ↗
90. Roychowdhury, V. P., Janes, D. B., Bandyopadhyay, S. & Xiaodong, W. Collective computational activity in self-assembled arrays of quantum dots: a novel neuromorphic architecture for nanoelectronics. IEEE Trans. Electron. Dev. 43, 1688–1699 (1996).10.1109/16.536815 ↗
91. Altaisky, M. V. et al. Towards a feasible implementation of quantum neural networks using quantum dots. Appl. Phys. Lett. 108, 103108 (2016).10.1063/1.4943622 ↗
92. Cao, Q. et al. Carbon nanotube transistors scaled to a 40-nanometer footprint. Science 356, 1369–1372 (2017).10.1126/science.aan2476 ↗
93. Joshi, J., Parker, A. C. & Hsu, C. A carbon nanotube cortical neuron with spike-timing-dependent plasticity. In Proc. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1651–1654 (IEEE, 2009).10.1109/IEMBS.2009.5333251 ↗
94. Kim, S., Yoon, J., Kim, H.-D. & Choi, S.-J. Carbon nanotube synaptic transistor network for pattern recognition. ACS Appl. Mater. Inter. 7, 25479–25486 (2015).10.1021/acsami.5b08541 ↗
95. Shen, A. M. et al. Analog neuromorphic module based on carbon nanotube synapses. ACS Nano 7, 6117–6122 (2013).10.1021/nn401946s ↗
96. Kim, K. et al. A carbon nanotube synapse with dynamic logic and learning. Adv. Mater. 25, 1693–1698 (2013).10.1002/adma.201203116 ↗
97. Feng, P. et al. Printed neuromorphic devices based on printed carbon nanotube thin-film transistors. Adv. Funct. Mater. 27, 1604447 (2017).10.1002/adfm.201604447 ↗
98. Danesh, C. D. et al. Synaptic resistors for concurrent inference and learning with high energy efficiency. Adv. Mater. 31, 1808032 (2019).10.1002/adma.201808032 ↗
99. Duan, X., Huang, Y. & Lieber, C. M. Nonvolatile memory and programmable logic from molecule-gated nanowires. Nano Lett. 2, 487–490 (2002).10.1021/nl025532n ↗
100. Younis, A. et al. High-performance nanocomposite based memristor with controlled quantum dots as charge traps. ACS Appl. Mater. Inter. 5, 2249–2254 (2013).10.1021/am400168m ↗