Memristive Artificial Synapses for Neuromorphic Computing
Corresponding Author: Xing’ao Li
Nano-Micro Letters,
Vol. 13 (2021), Article Number: 85
Abstract
Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
Highlights:
1 Synaptic devices that mimic synaptic functions are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.
2 The working mechanisms, progress, and application scenarios of synaptic devices based on electrical and optical signals are compared and analyzed.
3 The performances and future development of various synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- M.M. Waldrop, The chips are down for Moore’s law. Nature 530, 145–147 (2016). https://doi.org/10.1038/530144a
- M.A. Zidan, J.P. Strachan, W.D. Lu, The future of electronics based on memristive systems. Nat. Electron 1, 22–29 (2018). https://doi.org/10.1038/s41928-017-0006-8
- H.-S.P. Wong, T.N. Theis, the end of moore’s Law: A new beginning for information technology. Comput. Sci. Eng. 19(2), 41–50 (2017). https://doi.org/10.1109/MCSE.2017.29
- H. Yu, H. Wei, J. Gong, H. Han, M. Ma et al., Evolution of bio-inspired artificial synapses: materials, structures, and mechanisms. Small (2020). https://doi.org/10.1002/smll.202000041
- J.-A. Lee, V.M. Ho, K.C. Martin, The cell biology of synaptic plasticity. Science 334(6056), 623–628 (2011). https://doi.org/10.1126/science.1209236
- M. Tsodyks, C. Gilbert, Neural networks and perceptual learning. Nature 431, 775–781 (2004). https://doi.org/10.1038/nature03013
- D. Strukov, G. Indiveri, J. Grollier, S. Fusi, Building brain-inspired computing. Nat. Commun. 10, 4838 (2019). https://doi.org/10.1038/s41467-019-12521-x
- W.G. Regehr, L.F. Abbott, Synaptic computation. Nature 431, 796–803 (2004). https://doi.org/10.1038/nature03010
- S.L. Jackman, W.G. Regehr, The mechanisms and functions of synaptic facilitation. Neuron 94(3), 447–464 (2017). https://doi.org/10.1016/j.neuron.2017.02.047
- J.H. Schwartz, E.R. Kandel, T.M. Jessell, S.A. Siegelbaum, A.J. Hudspeth, Principles of Neural Science, 5th edn. (McGraw-Hill, New York, 2013), p. 185
- K.D. Miller, L.F. Abbott, S. Song, Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000). https://doi.org/10.1038/78829
- Y. Wang, L. Yin, W. Huang, Y. Li, S. Huang et al., Optoelectronic synaptic devices for neuromorphic computing. Adv. Intell. Syst. (2020). https://doi.org/10.1002/aisy.202000099
- H.L. Park, Y. Lee, N. Kim, D.G. Seo, G.T. Go et al., Flexible neuromorphic electronics for computing, soft robotics, and neuroprosthetics. Adv. Mater. 32(15), 1903558 (2020). https://doi.org/10.1002/adma.201903558
- T. Zhang, K. Yang, X. Xu, Y. Cai, Y. Yang et al., Memristive devices and networks for brain-inspired computing. Phys. Status. Solidi. RRL 13(8), 1900029 (2019). https://doi.org/10.1002/pssr.201970031
- Y. Li, Z. Wang, R. Midya, Q. Xia, J.J. Yang, Review of memristor devices in neuromorphic computing: materials sciences and device challenges. J. Phys. D: Appl. Phys. 51(50), 503002 (2018). https://doi.org/10.1088/1361-6463/aade3f
- J.-S. Lee, M.-K. Kim, Ferroelectric analog synaptic transistors. Nano Lett. 19(3), 2044–2050 (2019). https://doi.org/10.1021/acs.nanolett.9b00180
- F. Zhou, Z. Zhou, J. Chen, T.H. Choy, J. Wang et al., Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019). https://doi.org/10.1038/s41565-019-0501-3
- B.L. Jackson, B. Rajendran, G.S. Corrado, M. Breitwisch, G. Burr et al., Nanoscale electronic synapses using phase change devices. ACM J. Emerg. Technol. Comput. 9(2), 12 (2013). https://doi.org/10.1145/2463585.2463588
- F. Alibart, S. Pleutin, O. Bichler, C. Gamrat, T. Serrano-Gotarredona et al., A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater. 22(3), 609–616 (2012). https://doi.org/10.1002/adfm.201101935
- H. Tan, Z. Ni, W. Peng, S. Du, X. Liu et al., Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy 52, 422–430 (2018). https://doi.org/10.1016/j.nanoen.2018.08.018
- Y. Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria et al., A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017). https://doi.org/10.1038/nmat4856
- E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal et al., Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364(6440), 570–574 (2019). https://doi.org/10.1126/science.aaw5581
- C.S. Yang, D.S. Shang, N. Liu, G. Shi, X. Shen et al., A synaptic transistor based on quasi-2D molybdenum oxide. Adv. Mater. (2017). https://doi.org/10.1002/adma.201700906
- Z. Xiao, J. Huang, Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2, 1600100 (2016). https://doi.org/10.1002/aelm.201600100
- S. Gao, G. Liu, H. Yang, C. Hu, Q. Chen et al., An oxide schottky junction artificial optoelectronic synapse. ACS Nano 13(2), 2634–2642 (2019). https://doi.org/10.1021/acsnano.9b00340
- T. Kawauchi, S. Kano, M. Fujii, Electrically stimulated synaptic resistive switch in solution-processed silicon nanocrystal thin film: formation mechanism of oxygen vacancy filament for synaptic function. ACS Appl. Electron. Mater. 1(12), 2664–2670 (2019). https://doi.org/10.1021/acsaelm.9b00625
- H.K. He, R. Yang, W. Zhou, H.M. Huang, J. Xiong et al., Photonic potentiation and electric habituation in ultrathin memristive synapses based on monolayer MoS2. Small 14, 1800079 (2018). https://doi.org/10.1002/smll.201800079
- D. Kuzum, S. Yu, H.S. Wong, Synaptic electronics: materials, devices and applications. Nanotechnology 24(38), 382001 (2013). https://doi.org/10.1088/0957-4484/24/38/382001
- S. Lu, F. Zeng, W. Dong, A. Liu, X. Li et al., Controlling ion conductance and channels to achieve synaptic-like frequency selectivity. Nano-Micro Lett. 7, 121–126 (2015). https://doi.org/10.1007/s40820-014-0024-2
- S. Zhao, Z. Ni, H. Tan, Y. Wang, H. Jin et al., Electroluminescent synaptic devices with logic functions. Nano Energy 54, 383–389 (2018). https://doi.org/10.1016/j.nanoen.2018.10.018
- D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453, 80–83 (2008). https://doi.org/10.1038/nature06932
- Y. Yang, P. Gao, S. Gaba, T. Chang, X. Pan, W. Lu, Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012). https://doi.org/10.1038/ncomms1737
- R. Waser, R. Dittmann, G. Staikov, K. Szot, Redox-based resistive switching memories - nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21(25–26), 2632–2663 (2009). https://doi.org/10.1002/adma.200900375
- A. Citri, R.C. Malenka, Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). https://doi.org/10.1038/sj.npp.1301559
- X. Yan, L. Zhang, H. Chen, X. Li, J. Wang et al., Graphene oxide quantum dots based memristors with progressive conduction tuning for artificial synaptic learning. Adv. Funct. Mater. 28(40), 1803728 (2018). https://doi.org/10.1002/adfm.201803728
- X. Yan, Y. Pei, H. Chen, J. Zhao, Z. Zhou et al., Self-assembled networked PbS distribution quantum dots for resistive switching and artificial synapse performance boost of memristors. Adv. Mater. 31(7), 1805284 (2019). https://doi.org/10.1002/adma.201805284
- T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J.K. Gimzewski et al., Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011). https://doi.org/10.1038/nmat3054
- Y. Kaneko, Y. Nishitani, M. Ueda, Ferroelectric artificial synapses for recognition of a multishaded image. IEEE Trans. Electron Devices 61(8), 2827–2833 (2014). https://doi.org/10.1109/TED.2014.2331707
- S. Boyn, J. Grollier, G. Lecerf, B. Xu, N. Locatelli et al., Learning through ferroelectricdomain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017). https://doi.org/10.1038/ncomms14736
- S. Majumdar, H. Tan, Q.H. Qin, S.V. Dijken, Energy-efficient organic ferroelectric tunnel junction memristors for neuromorphic computing. Adv. Electron. Mater. 5(3), 1800795 (2019). https://doi.org/10.1002/aelm.201800795
- T.H. Lee, D. Loke, K.J. Huang, W.J. Wang, S.R. Elliott, Tailoring transient-amorphous states: towards fast and power-efficient phase-change memory and neuromorphic computing. Adv. Mater. 26(44), 7493–7498 (2014). https://doi.org/10.1002/adma.201402696
- Y. Li, Y. Zhong, L. Xu, X. Miao, Simple square pulses for implementing spike-timeing-dependent plasticity inphase-change memory. Phys. Status Solid RRL 9(7), 414–419 (2015). https://doi.org/10.1002/pssr.201510150
- A. Nayak, T. Ohno, T. Tsuruoka, K. Terabe, T. Hasegawa et al., Controlling the synaptic plasticity of a Cu2S gap-type atomic switch. Adv. Funct. Mater. 22(17), 3603–3613 (2012). https://doi.org/10.1002/adfm.201200640
- M. Suri, O. Bichler, D. Querlioz, G. Palma, E. Vianello et al., CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (cochlea) and visual (retina) cognitive processing applications. IEDM 13384039 (2013). https://doi.org/https://doi.org/10.1109/IEDM.2012.6479017
- F. Zeng, Y. Guo, W. Hu, Y. Tan, X. Zhang et al., Opportunity of the lead-free all-inorganic Cs3Cu2I5 perovskite film for memristor and neuromorphic computing applications. ACS Appl. Mater. Interfaces 12(20), 23094–23101 (2020). https://doi.org/10.1021/acsami.0c03106
- Z. Wang, M. Yin, T. Zhang, Y. Cai, Y. Wang et al., Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale 8, 14015–14022 (2016). https://doi.org/10.1039/C6NR00476H
- T. Chang, S.-H. Jo, W. Lu, Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5(9), 7669–7676 (2011). https://doi.org/10.1021/nn202983n
- W. Xu, H. Cho, Y.H. Kim, Y.T. Kim, C. Wolf et al., Organometal halide perovskite artificial synapses. Adv. Mater. 28(28), 5916–5922 (2016). https://doi.org/10.1002/adma.201506363
- A.S.S. Park, J. Kim, J. Noh, J. Jang, M. Jeon et al., Neuromorphic speech systems using advanced ReRAM-based synapse. IEDM 14062238 (2013). https://doi.org/https://doi.org/10.1109/IEDM.2013.6724692
- L.Q. Guo, H. Han, L.Q. Zhu, Y.B. Guo, F. Yu et al., Oxide neuromorphic transistors gated by polyvinyl alcohol solid electrolytes with ultralow power consumption. ACS Appl. Mater. Interfaces 11(31), 28352–28358 (2019). https://doi.org/10.1021/acsami.9b05717
- Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh et al., A bioinspired flexible organic artificial afferent nerve. Science 360(6392), 998–1003 (2018). https://doi.org/10.1126/science.aao0098
- S. Li, F. Zeng, C. Chen, H. Liu, G. Tang et al., Synaptic plasticity and learning behaviors mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J. Mater. Chem. C 1(34), 5292–5298 (2013). https://doi.org/10.1039/C3TC30575A
- W. Xu, S. Min, H. Hwang, T.W. Lee, Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2(6), e1501326 (2016). https://doi.org/10.1126/sciadv.1501326
- W. Xu, T.L. Nguyue, Y. Kim, C. Wolf, R. Pfattner et al., Ultrasensitive artificial synapse based on conjugated polyelectrolyte. Nano Energy 48, 575–581 (2018). https://doi.org/10.1016/j.nanoen.2018.02.058
- F. Zeng, S. Li, J. Yang, F. Pan, D. Guo, Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell. RSC Adv. 4(29), 14822–14828 (2014). https://doi.org/10.1039/C3RA46679E
- D. Seo, Y. Lee, G. Go, M. Pei, S. Jung et al., Versatile neuromorphic electronics by modulating synaptic decay of single organic synaptic transistor: From artificial neural networks to neuro-prosthetics. Nano Energy 65, 104035 (2019). https://doi.org/10.1016/j.nanoen.2019.104035
- G. Go, Y. Lee, D. Seo, M. Pei, W. Lee et al., Achieving microstructure-controlled synaptic plasticity and long-term retention in ion-gel-gated organic synaptic transistors. Adv. Intell. Syst. 2(11), 2000012 (2020). https://doi.org/10.1002/aisy.202000012
- J. Tang, F. Yuan, X. Shen, Z. Wang, M. Rao et al., Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31(49), 1902761 (2019). https://doi.org/10.1002/adma.201902761
- J.-U. Woo, H.-G. Hwang, S.-M. Park, T.-G. Lee, S. Nahm, Improvement in conductance modulation linearity of artificial synapses based on NaNbO3 memristor. Appl. Mater. Today 19, 100582 (2020). https://doi.org/10.1016/j.apmt.2020.100582
- D.-T. Wang, Y.-W. Dai, J. Xu, L. Chen, Q.-Q. Sun et al., Resistive switching and synaptic behaviors of TaN/Al2O3/ZnO/ITO flexible devices with embedded Ag nanoparticles. IEEE Electron Device Lett. 37(7), 16105146 (2016). https://doi.org/10.1109/LED.2016.2570279
- S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder et al., Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010). https://doi.org/10.1021/nl904092h
- D.J. Kim, H. Lu, S. Ryu, C.W. Bark, C.B. Eom et al., Ferroelectric tunnel memristor. Nano Lett. 12(11), 5697–5702 (2012). https://doi.org/10.1021/nl302912t
- S.V. Kalinin, B.J. Rodriguez, A.Y. Borisevich, A.P. Baddorf, N. Balke et al., Defect-mediated polarization switching in ferroelectrics and related materials: from mesoscopic mechanisms to atomistic control. Adv. Mater. 22(3), 314–322 (2010). https://doi.org/10.1002/adma.200900813
- A. Chanthbouala, V. Garcia, R.O. Cherifi, K. Bouzehouane, S. Fusil et al., A ferroelectric memristor. Nat. Mater. 11(10), 860–864 (2012). https://doi.org/10.1038/nmat3415
- J. Guyonnet, I. Gaponenko, S. Gariglio, P. Paruch, Conduction at domain walls in insulating Pb(Zr0.2 Ti0.8)O3 thin films. Adv. Mater. 23(45), 5377–5382 (2011). https://doi.org/10.1002/adma.201102254
- J. Li, C. Ge, J. Du, C. Wang, G. Yang et al., Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing. Adv. Mater. 32(7), 1905764 (2020). https://doi.org/10.1002/adma.201905764
- G. Zhong, M. Zi, C. Ren, Q. Xiao, M. Tang et al., Flexible electronic synapse enabled by ferroelectric field effect transistor for robust neuromorphic computing. Appl. Phys. Lett. 117(9), 092903 (2020). https://doi.org/10.1063/5.0013638
- Y. Shi, S. Fong, H.-S.P. Wong, D. Kuzum, Synaptic devices based on phase-change memory, in Neuro-inspired Computing Using Resistive Synaptic Devices. ed. by S. Yu (Springer, Berlin, 2017), pp. 19–51. https://doi.org/10.1007/978-3-319-54313-0_2
- S.-H. Lee, Y. Jung, A.T. Jennings, R. Agarwal, Core-shell heterostructured phase change nanowire multistate memory. Nano Lett. 8(7), 2056–2062 (2008). https://doi.org/10.1021/nl801482z
- N. Yamada, M. Wuttig, Phase change materials for rewriteable data storage. Nat. Mater. 6(11), 824–832 (2007). https://doi.org/10.1038/nmat2009
- T. Tuma, A. Pantazi, M.L. Gallo, A. Sebastian, E. Eleftheriou, Stochastic phase-change neurons. Nat. Nanotechnol. 11(8), 693–699 (2016). https://doi.org/10.1038/nnano.2016.70
- K. Ren, R. Li, X. Chen, Y. Wang, J. Shen et al., Controllable set process in O-Ti-Sb-Te based phase change memory for synaptic application. Appl. Phys. Lett. 112, 073106 (2018). https://doi.org/10.1063/1.5018513
- D. Kuzum, R.G. Jeyasingh, B. Lee, H.S. Wong, Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12(5), 2179–2186 (2012). https://doi.org/10.1021/nl201040y
- O. Bichler,M. Suri, D. Querlioz, O. Cueto, L. Perniola et al., Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. IEDM 12504168 (2012). https://doi.org/https://doi.org/10.1109/IEDM.2011.6131488
- L.V. Tho, K.J. Baeg, Y.Y. Noh, Organic nano-floating-gate transistor memory with metal nanoparticles. Nano Convergence 3, 10 (2016). https://doi.org/10.1186/s40580-016-0069-7
- D. Sarkar, J. Tao, W. Wang, Q. Lin, M. Yeung et al., Mimicking biological synaptic functionality with an indium phosphide synaptic device on silicon for scalable neuromorphic computing. ACS Nano 12(2), 1656–1663 (2018). https://doi.org/10.1021/acsnano.7b08272
- M. Zhang, Z. Fan, X. Jiang, H. Zhu, L. Chen et al., MoS2-based charge-trapping synaptic device with electrical and optical modulated conductance. Nanophotonics 9(8), 2475–2486 (2020). https://doi.org/10.1515/nanoph-2019-0548
- S.-R. Zhang, L. Zhou, J.-Y. Mao, Y. Ren, J.-Q. Yang et al., Artificial synapse emulated by charge trapping-based resistive switching device. Adv. Mater. Technol. 4(2), 1800342 (2019). https://doi.org/10.1002/admt.201800342
- F. Alibart, S. Pleutin, D. Guerin, C. Novembre, S. Lenfant et al., An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater. 20, 330–337 (2010). https://doi.org/10.1002/adfm.200901335
- J. Jadwiszczak, D. Keane, P.R. Maguire, C.P. Cullen, H. Zhang et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13(12), 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
- C. Liu, H. Chen, S. Wang, H. Zhang, Q. Liu et al., Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15(7), 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3
- G.M. Marega, Y. Zhao, A. Avsar, Z. Wang, M. Tripathi et al., Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020). https://doi.org/10.1038/s41586-020-2861-0
- V.K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M.E. Beck et al., Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018). https://doi.org/10.1038/nature25747
- S. Wang, D.W. Zhang, P. Zhou, Two-dimensional materials for synaptic electronics and neuromorphic systems. Sci. Bull. 64(15), 1056–1066 (2019). https://doi.org/10.1016/j.scib.2019.01.016
- Y. Shi, C. Pan, V. Chen, N. Raghavan, K.L. Pey et al., Coexistence of volatile and non-volatile resistive switching in 2D h-BN based electronic synapses. IEDM 17524736 (2018). https://doi.org/https://doi.org/10.1109/IEDM.2017.8268333
- Y. Shi, X. Liang, B. Yuan, V. Chen, H. Li et al., Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1(8), 458–465 (2018). https://doi.org/10.1038/s41928-018-0118-9
- X. Zhu, D. Li, X. Liang, W.D. Lu, Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18(2), 141–148 (2019). https://doi.org/10.1038/s41563-018-0248-5
- S. Seo, B.S. Kang, J.-J. Lee, H.-J. Ryu, S. Kim et al., Artificial van der waals hybrid synapse and its application to acoustic pattern recognition. Nat. Commun. 11, 3936 (2020). https://doi.org/10.1038/s41467-020-17849-3
- H. Tian, Q. Guo, Y. Xie, H. Zhao, C. Li et al., Anisotropic black phosphorus synaptic device for neuromorphic applications. Adv. Mater. 28(25), 4991–4997 (2016). https://doi.org/10.1002/adma.201600166
- X. Zhu, W.D. Lu, Optogenetics-inspired tunable synaptic functions in memristors. ACS Nano 12(2), 1242–1249 (2018). https://doi.org/10.1021/acsnano.7b07317
- S. Seo, S.H. Jo, S. Kim, J. Shim, S. Oh et al., Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat. Commun. 9(1), 5106 (2018). https://doi.org/10.1038/s41467-018-07572-5
- M. Lee, W. Lee, S. Choi, J.W. Jo, J. Kim et al., Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semiconductors and their persistent photoconductivity. Adv. Mater. 29(28), 1700951 (2017). https://doi.org/10.1002/adma.201700951
- D.C. Hu, R. Yang, L. Jiang, X. Guo, Memristive synapses with photoelectric plasticity realized in ZnO1-x/AlOy heterojunction. ACS Appl. Mater. Interfaces 10(7), 6463–6470 (2018). https://doi.org/10.1021/acsami.8b01036
- M. Kumar, S. Abbas, J. Kim, All-oxide-based highly transparent photonic synapse for neuromorphic computing. ACS Appl. Mater. Interfaces 10(40), 34370–34376 (2018). https://doi.org/10.1021/acsami.8b10870
- H.K. Li, T.P. Chen, P. Liu, S.G. Hu, Y. Liu et al., A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnOx–Al2O3 thin film structure. J. Appl. Phys. 119, 244505 (2016). https://doi.org/10.1063/1.4955042
- S. Song, M. Kim, G. Yoo, S.-M. Kwon, J.-S. Heo et al., Solution-processed oxide semiconductor-based artificial optoelectronic synapse array for spatiotemporal synaptic integration. J. Alloy. Compd. 857, 158027 (2021). https://doi.org/10.1016/j.jallcom.2020.158027
- L. Yin, W. Huang, R. Xiao, W. Peng, Y. Zhu et al., Optically stimulated synaptic devices based on the hybrid structure of silicon nanomembrane and perovskite. Nano Lett. 20(5), 3378–3387 (2020). https://doi.org/10.1021/acs.nanolett.0c00298
- S. Dai, X. Wu, D. Liu, Y. Chu, K. Wang et al., Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl. Mater. Interfaces 10(25), 21472–21480 (2018). https://doi.org/10.1021/acsami.8b05036
- Y. Sun, L. Qian, D. Xie, Y. Lin, M. Sun et al., Photoelectric synaptic plasticity realized by 2D perovskite. Adv. Funct. Mater. 29(28), 1902538 (2019). https://doi.org/10.1002/adfm.201902538
- L. Yin, C. Han, Q. Zhang, Z. Ni, S. Zhao et al., Synaptic silicon-nanocrystal phototransistors for neuromorphic computing. Nano Energy 63, 103859 (2019). https://doi.org/10.1016/j.nanoen.2019.103859
- S. Qin, F. Wang, Y. Liu, Q. Wan, X. Wang et al., A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater. 4(3), 035022 (2017). https://doi.org/10.1088/2053-1583/aa805e
- J. Jiang, W. Hu, D. Xie, J. Yang, J. He et al., 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration. Nanoscale 11(3), 1360–1369 (2019). https://doi.org/10.1039/C8NR07133K
- L. Chu, W. Ahmad, W. Liu, J. Yang, R. Zhang et al., Lead-free halide double perovskite materials: a new superstar toward green and stable optoelectronic applications. Nano-Micro Lett. 11, 16 (2019). https://doi.org/10.1007/s40820-019-0244-6
- Y. Fang, Q. Dong, Y. Shao, P. Mulligan, J. Qiu et al., Electron-hole diffusion lengths >175 μm in solution-grown CH3NH3PbI3 single crystals. Science 347(6225), 967–970 (2015). https://doi.org/10.1126/science.aaa5760
- A. Zavabeti, A. Jannat, L. Zhong, A.A. Haidry, Z. Yao et al., Two-dimensional materials in large-areas: synthesis, properties and applications. Nano-Micro Lett. 12, 66 (2020). https://doi.org/10.1007/s40820-020-0402-x
- Z. Ni, X. Pi, S. Zhou, T. Nozaki, B. Grandidier et al., Size-dependent structures and optical absorption of boron-hyperdoped silicon nanocrystals. Adv. Opt. Mater. 4(5), 700–707 (2016). https://doi.org/10.1002/adom.201500706
- R. Islam, P.-Y. Chen, W. Wan, H.-Y. Chen, B. Gao et al., Device and materials requirements for neuromorphic computing. J. Phys. D: Appl. Phys. 52, 113001 (2019). https://doi.org/10.1088/1361-6463/aaf784
- W. Huang, P. Hang, Y. Wang, K. Wang, S. Han et al., Zero-power optoelectronic synaptic devices. Nano Energy 73, 104790 (2020). https://doi.org/10.1016/j.nanoen.2020.104790
- J. Zhang, S. Dai, Y. Zhao, J. Zhang, J. Huang, Recent progress in photonic synapses for neuromorphic systems. Adv. Intell. Syst. 2, 1900136 (2020). https://doi.org/10.1002/aisy.201900136
- C. Ríos, Z. Cheng, W.H.P. Pernice, C.D. Wright, H. Bhaskaran, On-chip photonic synapse. Sci. Adv. 3(9), 1700160 (2017). https://doi.org/10.1126/sciadv.1700160
- G. Pacchioni, Oxygen vacancy: the invisible agent on oxide surfaces. ChemPhysChem 4(10), 1041–1047 (2003). https://doi.org/10.1002/cphc.200300835
- H. Song, G. Kang, Y. Kang, S. Han, the nature of the oxygen vacancy in amorphous oxide semiconductors: shallow versus deep. Phys. Status Solidi 256(3), 1800486 (2019). https://doi.org/10.1002/pssb.201800486
- H.-K. Noh, B. Ryu, E.-A. Choi, K.J. Chang, O-vacancy as the origin of negative bias illumination stress instability in amorphous InGaZnO thin film transistors. Appl. Phys. Lett. 97, 022108 (2010). https://doi.org/10.1063/1.3464964
- J.J. Yu, L.Y. Liang, L.X. Hu, H.X. Duan, W.H. Wu et al., Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation. Nano Energy 62, 772–780 (2019). https://doi.org/10.1016/j.nanoen.2019.06.007
- Q. Wu, J. Wang, J. Cao, C. Lu, G. Yang et al., Photoelectric plasticity in oxide thin film transistors with tunable synaptic functions. Adv. Electron. Mater. 4(12), 1800556 (2018). https://doi.org/10.1002/aelm.201800556
- J. Yu, K. Javaid, L. Liang, W. Wu, Y. Liang et al., High-performance visible-blind ultraviolet photodetector based on IGZO TFT coupled with p-n heterojunction. ACS Appl. Mater. Interfaces 10(9), 8102–8109 (2018). https://doi.org/10.1021/acsami.7b16498
- H. Tan, G. Liu, X. Zhu, H. Yang, B. Chen et al., An optoelectronic resistive switching memory with integrated demodulating and arithmetic functions. Adv. Mater. 27(17), 2797–2803 (2015). https://doi.org/10.1002/adma.201500039
- X. Han, Z. Xu, W. Wu, X. Liu, P. Yan et al., Recent progress in optoelectronic synapses for artificial visual-perception system. Small Struct. 1(3), 2000029 (2020). https://doi.org/10.1002/sstr.202000029
- S.F. Leung, K.T. Ho, P.K. Kung, V.K.S. Hsiao, H.N. Alshareef et al., A self-poweredand flexible organometallic halide perovskite photodetector with very high detectivity. Adv. Mater. 3(8), 1704611 (2018). https://doi.org/10.1002/adma.201704611
- Y. Ogomi, A. Morita, S. Tsukamoto, T. Saitho, N. Fujikawa et al., CH3NH3SnxPb(1–x)I3 perovskite solar cells covering up to 1060 nm. J. Phys. Chem. Lett. 5(6), 1004–1011 (2014). https://doi.org/10.1021/jz5002117
- B.R. Sutherland, A.K. Johnston, A.H. Ip, J. Xu, V. Adinolfi et al., Sensitive, fast, and stable perovskite photodetectors exploiting interface engineering. ACS Photonics 2(8), 1117–1123 (2015). https://doi.org/10.1021/acsphotonics.5b00164
- J. Sun, Y. Choi, Y.J. Choi, S. Kim, J.H. Park et al., 2D-organic hybrid heterostructures for optoelectronic applications. Adv. Mater. 31(34), 1803831 (2019). https://doi.org/10.1002/adma.201803831
- Q. Zhao, W. Wang, F. Carrascoso-Plana, W. Jie, T. Wang et al., The role of traps in the photocurrent generation mechanism in thin InSe photodetectors. Mater. Horizons 7(1), 252–262 (2020). https://doi.org/10.1039/C9MH01020C
- M. Dasog, L.V. Titova, F.A. Hegmann, J.G.C. Veinot, Size vs surface tuning the photoluminescence of freestanding silicon nanocrystals across the visible spectrum via surface groups. ACS Nano 8(9), 9636–9648 (2014). https://doi.org/10.1021/nn504109a
- T.H. Han, S. Tan, J. Xue, L. Meng, J.W. Lee et al., Interface and defect engineering for metal halide perovskite optoelectronic devices. Adv. Mater. 31(47), 1803515 (2019). https://doi.org/10.1002/adma.201803515
- T.H. Tsai, Z.Y. Liang, Y.C. Lin, C.C. Wang, K.I. Lin et al., Photogating WS2 photodetectors using embedded WSe2 charge puddles. ACS Nano 14(4), 4559–4566 (2020). https://doi.org/10.1021/acsnano.0c00098
- L. Qian, Y. Sun, M. Wu, C. Li, D. Xie et al., A lead-free two-dimensional perovskite for a high-performance flexible photoconductor and a light-stimulated synaptic device. Nanoscale 10(15), 6837–6843 (2018). https://doi.org/10.1039/c8nr00914g
- B. Li, W. Wei, X. Yan, X. Zhang, P. Liu et al., Mimicking synaptic functionality with an InAs nanowire phototransistor. Nanotechnology 29, 464004 (2018). https://doi.org/10.1088/1361-6528/aadf63
- K. Wang, S. Dai, Y. Zhao, Y. Wang, C. Liu et al., Light-stimulated synaptic transistors fabricated by a facile solution process based on inorganic perovskite quantum dots and organic semiconductors. Small 15(11), 1900010 (2019). https://doi.org/10.1002/smll.201900010
- S.R. Ovshinsky, Optically induced phase changes in amorphous materials. J. Non-Cryst. Solids. 141, 200–203 (1992). https://doi.org/10.1016/S0022-3093(05)80534-4
- C. Ríos, M. Stegmaier, P. Hosseini, D. Wang, T. Scherer et al., Integrated all-photonic non-volatile multi-level memory. Nat. Photonics 9, 725–732 (2015). https://doi.org/10.1038/nphoton.2015.182
- C. Qian, S. Oh, Y. Choi, J.-H. Kim, J. Sun et al., Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing. Nano Energy 66, 104095 (2019). https://doi.org/10.1016/j.nanoen.2019.104095
- S. Ham, S. Choi, H. Cho, S.-I. Na, G. Wang, Photonic organolead halide perovskite artificial synapse capable of accelerated learning at low power inspired by dopamine-facilitated synaptic activity. Adv. Funct. Mater. 29(5), 1806646 (2019). https://doi.org/10.1002/adfm.201806646
- L.K. Ono, E.J. Juarez-Perez, Y. Qi, Progress on perovskite materials and solar cells with mixed cations and halide anions. ACS Appl. Mater. Interfaces 9(36), 30197–30246 (2017). https://doi.org/10.1021/acsami.7b06001
- Y. Xie, E. Wu, J. Zhang, H. Zhang, X. Hu et al., Dynamically controllable polarity modulation of MoTe2 field-effect transistors through ultraviolet light and electrostatic activation. Sci. Adv. 5(5), aav3430 (2019). https://doi.org/10.1126/sciadv.aav3430
- R. Inoue, S. Ishikawa, R. Imura, Y. Kitanaka, T. Oguchi et al., Giant photovoltaic effect of ferroelectric domain walls in perovskite single crystals. Sci. Rep. 5, 14741 (2015). https://doi.org/10.1038/srep14741
- Y. Wang, Z. Lv, J. Chen, Z. Wang, Y. Zhou et al., Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30(38), 1802883 (2018). https://doi.org/10.1002/adma.201802883
- S. Wang, C. Chen, Z. Yu, Y. He, X. Chen et al., A MoS2/PTCDA hybrid heterojunction synapse with efficient photoelectric dual modulation and versatility. Adv. Mater. 31(3), 1806227 (2019). https://doi.org/10.1002/adma.201806227
- S. Wang, X. Hou, L. Liu, J. Li, Y. Shan et al., A photoelectric-stimulated MoS2 transistor for neuromorphic engineering. Research 2019, 1618798 (2019). https://doi.org/https://doi.org/10.34133/2019/1618798
- Y. He, Y. Yang, S. Nie, Y. Shi, Q. Wan, Light stimulated IGZO-based electric-double-layer transistors for photoelectric neuromorphic devices. IEEE Electron Device Lett. 39(6), 897–900 (2018). https://doi.org/10.1109/LED.2018.2824339
- J. Sun, S. Oh, Y. Choi, S. Seo, M.J. Oh et al., Optoelectronic synapse based on IGZO-alkylated graphene oxide hybrid structure. Adv. Funct. Mater. 28(47), 1804397 (2018). https://doi.org/10.1002/adfm.201804397
- T. Morera-Herreras, Y. Gioanni, S. Perez, G. Vignoud, L. Venance, Environmental enrichment shapes striatal spike-timing-dependent plasticity in vivo. Sci. Rep. 9(1), 19451 (2019). https://doi.org/10.1038/s41598-019-55842-z
- R.A. John, N. Yantara, Y.F. Ng, G. Narasimman, E. Mosconi et al., Ionotronic halide perovskite drift-diffusive synapses for low-power neuromorphic computation. Adv. Mater. 30, 1805454 (2018). https://doi.org/10.1002/adma.201805454
- D. Przyczyna, M. Lis, K. Pilarczyk, K. Szacilowski, Hardware realization of the pattern recognition with an artificial neuromorphic device exhibiting a short-term memory. Molecules 24(15), 2738 (2019). https://doi.org/10.3390/molecules24152738
- F. Alibart, E. Zamanidoost, D.B. Strukov, Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4(1), 2072 (2013). https://doi.org/10.1038/ncomms3072
- P. Yao, H. Wu, B. Gao, S.B. Eryilmaz, X. Huang et al., Face classification using electronic synapses. Nat. Commun. 18, 15199 (2017). https://doi.org/10.1038/ncomms15199
- M. London, M. Häusser, Dendritic computation. Annu. Rev. Neurosci. 28(1), 503–532 (2005). https://doi.org/10.1146/annurev.neuro.28.061604.135703
- D. Hao, J. Zhang, S. Dai, J. Zhang, J. Huang, Perovskite/organic semiconductor-based photonic synaptic transistor for artificial visual system. ACS Appl. Mater. Interfaces 12(35), 39487–39495 (2020). https://doi.org/10.1021/acsami.0c10851
- A. Gruart, R. Leal-Campanario, J.C. Lopez-Ramos, J.M. Delgado-Garcia, Functional basis of associative learning and its relationships with long-term potentiation evoked in the involved neural circuits: lessons from studies in behaving mammals. Neurobiol. Learn. Mem. 124, 3–18 (2015). https://doi.org/10.1016/j.nlm.2015.04.006
- J. Rushen, Using aversion learning techniques to assess the mental state, suffering, and welfare of farm animals. J. Anim. Sci. 74(8), 1990–1995 (1990). https://doi.org/10.2527/1996.7481990x
- R.A. John, F. Liu, N.A. Chien, M.R. Kulkarni, C. Zhu et al., Synergistic gating of electro-iono-photoactive 2D chalcogenide neuristors: coexistence of hebbian and homeostatic synaptic metaplasticity. Adv. Mater. 30(25), 1800220 (2018). https://doi.org/10.1002/adma.201800220
- L.Q. Zhu, C.J. Wan, L.Q. Guo, Y. Shi, Q. Wan, Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 5(1), 3158 (2014). https://doi.org/10.1038/ncomms4158
- S. Herculano-Houzel, The human brain in numbers: a linearly scaled-up primate brain. Front. Neurosci. 3(31), 31 (2009). https://doi.org/10.3389/neuro.09.031.2009
- S. Park, M. Chu, J. Kim, J. Noh, M. Jeon et al., Electronic system with memristive synapses for pattern recognition. Sci. Rep. 5, 10123 (2015). https://doi.org/10.1038/srep10123
- J. Zhou, N. Liu, L. Zhu, Y. Shi, Q. Wan, Energy-efficient artificial synapses based on flexible IGZO electric-double-layer transistors. IEEE Electron Device Lett. 36(2), 198–200 (2015). https://doi.org/10.1109/LED.2014.2381631
- F. Gül, Addressing the sneak-path problem in crossbar RRAM devices using memristor-based one schottky diode-one resistor array. Results Phys. 12, 1091–1096 (2019). https://doi.org/10.1016/j.rinp.2018.12.092
References
M.M. Waldrop, The chips are down for Moore’s law. Nature 530, 145–147 (2016). https://doi.org/10.1038/530144a
M.A. Zidan, J.P. Strachan, W.D. Lu, The future of electronics based on memristive systems. Nat. Electron 1, 22–29 (2018). https://doi.org/10.1038/s41928-017-0006-8
H.-S.P. Wong, T.N. Theis, the end of moore’s Law: A new beginning for information technology. Comput. Sci. Eng. 19(2), 41–50 (2017). https://doi.org/10.1109/MCSE.2017.29
H. Yu, H. Wei, J. Gong, H. Han, M. Ma et al., Evolution of bio-inspired artificial synapses: materials, structures, and mechanisms. Small (2020). https://doi.org/10.1002/smll.202000041
J.-A. Lee, V.M. Ho, K.C. Martin, The cell biology of synaptic plasticity. Science 334(6056), 623–628 (2011). https://doi.org/10.1126/science.1209236
M. Tsodyks, C. Gilbert, Neural networks and perceptual learning. Nature 431, 775–781 (2004). https://doi.org/10.1038/nature03013
D. Strukov, G. Indiveri, J. Grollier, S. Fusi, Building brain-inspired computing. Nat. Commun. 10, 4838 (2019). https://doi.org/10.1038/s41467-019-12521-x
W.G. Regehr, L.F. Abbott, Synaptic computation. Nature 431, 796–803 (2004). https://doi.org/10.1038/nature03010
S.L. Jackman, W.G. Regehr, The mechanisms and functions of synaptic facilitation. Neuron 94(3), 447–464 (2017). https://doi.org/10.1016/j.neuron.2017.02.047
J.H. Schwartz, E.R. Kandel, T.M. Jessell, S.A. Siegelbaum, A.J. Hudspeth, Principles of Neural Science, 5th edn. (McGraw-Hill, New York, 2013), p. 185
K.D. Miller, L.F. Abbott, S. Song, Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000). https://doi.org/10.1038/78829
Y. Wang, L. Yin, W. Huang, Y. Li, S. Huang et al., Optoelectronic synaptic devices for neuromorphic computing. Adv. Intell. Syst. (2020). https://doi.org/10.1002/aisy.202000099
H.L. Park, Y. Lee, N. Kim, D.G. Seo, G.T. Go et al., Flexible neuromorphic electronics for computing, soft robotics, and neuroprosthetics. Adv. Mater. 32(15), 1903558 (2020). https://doi.org/10.1002/adma.201903558
T. Zhang, K. Yang, X. Xu, Y. Cai, Y. Yang et al., Memristive devices and networks for brain-inspired computing. Phys. Status. Solidi. RRL 13(8), 1900029 (2019). https://doi.org/10.1002/pssr.201970031
Y. Li, Z. Wang, R. Midya, Q. Xia, J.J. Yang, Review of memristor devices in neuromorphic computing: materials sciences and device challenges. J. Phys. D: Appl. Phys. 51(50), 503002 (2018). https://doi.org/10.1088/1361-6463/aade3f
J.-S. Lee, M.-K. Kim, Ferroelectric analog synaptic transistors. Nano Lett. 19(3), 2044–2050 (2019). https://doi.org/10.1021/acs.nanolett.9b00180
F. Zhou, Z. Zhou, J. Chen, T.H. Choy, J. Wang et al., Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019). https://doi.org/10.1038/s41565-019-0501-3
B.L. Jackson, B. Rajendran, G.S. Corrado, M. Breitwisch, G. Burr et al., Nanoscale electronic synapses using phase change devices. ACM J. Emerg. Technol. Comput. 9(2), 12 (2013). https://doi.org/10.1145/2463585.2463588
F. Alibart, S. Pleutin, O. Bichler, C. Gamrat, T. Serrano-Gotarredona et al., A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater. 22(3), 609–616 (2012). https://doi.org/10.1002/adfm.201101935
H. Tan, Z. Ni, W. Peng, S. Du, X. Liu et al., Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy 52, 422–430 (2018). https://doi.org/10.1016/j.nanoen.2018.08.018
Y. Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria et al., A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017). https://doi.org/10.1038/nmat4856
E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal et al., Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364(6440), 570–574 (2019). https://doi.org/10.1126/science.aaw5581
C.S. Yang, D.S. Shang, N. Liu, G. Shi, X. Shen et al., A synaptic transistor based on quasi-2D molybdenum oxide. Adv. Mater. (2017). https://doi.org/10.1002/adma.201700906
Z. Xiao, J. Huang, Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2, 1600100 (2016). https://doi.org/10.1002/aelm.201600100
S. Gao, G. Liu, H. Yang, C. Hu, Q. Chen et al., An oxide schottky junction artificial optoelectronic synapse. ACS Nano 13(2), 2634–2642 (2019). https://doi.org/10.1021/acsnano.9b00340
T. Kawauchi, S. Kano, M. Fujii, Electrically stimulated synaptic resistive switch in solution-processed silicon nanocrystal thin film: formation mechanism of oxygen vacancy filament for synaptic function. ACS Appl. Electron. Mater. 1(12), 2664–2670 (2019). https://doi.org/10.1021/acsaelm.9b00625
H.K. He, R. Yang, W. Zhou, H.M. Huang, J. Xiong et al., Photonic potentiation and electric habituation in ultrathin memristive synapses based on monolayer MoS2. Small 14, 1800079 (2018). https://doi.org/10.1002/smll.201800079
D. Kuzum, S. Yu, H.S. Wong, Synaptic electronics: materials, devices and applications. Nanotechnology 24(38), 382001 (2013). https://doi.org/10.1088/0957-4484/24/38/382001
S. Lu, F. Zeng, W. Dong, A. Liu, X. Li et al., Controlling ion conductance and channels to achieve synaptic-like frequency selectivity. Nano-Micro Lett. 7, 121–126 (2015). https://doi.org/10.1007/s40820-014-0024-2
S. Zhao, Z. Ni, H. Tan, Y. Wang, H. Jin et al., Electroluminescent synaptic devices with logic functions. Nano Energy 54, 383–389 (2018). https://doi.org/10.1016/j.nanoen.2018.10.018
D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453, 80–83 (2008). https://doi.org/10.1038/nature06932
Y. Yang, P. Gao, S. Gaba, T. Chang, X. Pan, W. Lu, Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012). https://doi.org/10.1038/ncomms1737
R. Waser, R. Dittmann, G. Staikov, K. Szot, Redox-based resistive switching memories - nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21(25–26), 2632–2663 (2009). https://doi.org/10.1002/adma.200900375
A. Citri, R.C. Malenka, Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). https://doi.org/10.1038/sj.npp.1301559
X. Yan, L. Zhang, H. Chen, X. Li, J. Wang et al., Graphene oxide quantum dots based memristors with progressive conduction tuning for artificial synaptic learning. Adv. Funct. Mater. 28(40), 1803728 (2018). https://doi.org/10.1002/adfm.201803728
X. Yan, Y. Pei, H. Chen, J. Zhao, Z. Zhou et al., Self-assembled networked PbS distribution quantum dots for resistive switching and artificial synapse performance boost of memristors. Adv. Mater. 31(7), 1805284 (2019). https://doi.org/10.1002/adma.201805284
T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J.K. Gimzewski et al., Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011). https://doi.org/10.1038/nmat3054
Y. Kaneko, Y. Nishitani, M. Ueda, Ferroelectric artificial synapses for recognition of a multishaded image. IEEE Trans. Electron Devices 61(8), 2827–2833 (2014). https://doi.org/10.1109/TED.2014.2331707
S. Boyn, J. Grollier, G. Lecerf, B. Xu, N. Locatelli et al., Learning through ferroelectricdomain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017). https://doi.org/10.1038/ncomms14736
S. Majumdar, H. Tan, Q.H. Qin, S.V. Dijken, Energy-efficient organic ferroelectric tunnel junction memristors for neuromorphic computing. Adv. Electron. Mater. 5(3), 1800795 (2019). https://doi.org/10.1002/aelm.201800795
T.H. Lee, D. Loke, K.J. Huang, W.J. Wang, S.R. Elliott, Tailoring transient-amorphous states: towards fast and power-efficient phase-change memory and neuromorphic computing. Adv. Mater. 26(44), 7493–7498 (2014). https://doi.org/10.1002/adma.201402696
Y. Li, Y. Zhong, L. Xu, X. Miao, Simple square pulses for implementing spike-timeing-dependent plasticity inphase-change memory. Phys. Status Solid RRL 9(7), 414–419 (2015). https://doi.org/10.1002/pssr.201510150
A. Nayak, T. Ohno, T. Tsuruoka, K. Terabe, T. Hasegawa et al., Controlling the synaptic plasticity of a Cu2S gap-type atomic switch. Adv. Funct. Mater. 22(17), 3603–3613 (2012). https://doi.org/10.1002/adfm.201200640
M. Suri, O. Bichler, D. Querlioz, G. Palma, E. Vianello et al., CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (cochlea) and visual (retina) cognitive processing applications. IEDM 13384039 (2013). https://doi.org/https://doi.org/10.1109/IEDM.2012.6479017
F. Zeng, Y. Guo, W. Hu, Y. Tan, X. Zhang et al., Opportunity of the lead-free all-inorganic Cs3Cu2I5 perovskite film for memristor and neuromorphic computing applications. ACS Appl. Mater. Interfaces 12(20), 23094–23101 (2020). https://doi.org/10.1021/acsami.0c03106
Z. Wang, M. Yin, T. Zhang, Y. Cai, Y. Wang et al., Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale 8, 14015–14022 (2016). https://doi.org/10.1039/C6NR00476H
T. Chang, S.-H. Jo, W. Lu, Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5(9), 7669–7676 (2011). https://doi.org/10.1021/nn202983n
W. Xu, H. Cho, Y.H. Kim, Y.T. Kim, C. Wolf et al., Organometal halide perovskite artificial synapses. Adv. Mater. 28(28), 5916–5922 (2016). https://doi.org/10.1002/adma.201506363
A.S.S. Park, J. Kim, J. Noh, J. Jang, M. Jeon et al., Neuromorphic speech systems using advanced ReRAM-based synapse. IEDM 14062238 (2013). https://doi.org/https://doi.org/10.1109/IEDM.2013.6724692
L.Q. Guo, H. Han, L.Q. Zhu, Y.B. Guo, F. Yu et al., Oxide neuromorphic transistors gated by polyvinyl alcohol solid electrolytes with ultralow power consumption. ACS Appl. Mater. Interfaces 11(31), 28352–28358 (2019). https://doi.org/10.1021/acsami.9b05717
Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh et al., A bioinspired flexible organic artificial afferent nerve. Science 360(6392), 998–1003 (2018). https://doi.org/10.1126/science.aao0098
S. Li, F. Zeng, C. Chen, H. Liu, G. Tang et al., Synaptic plasticity and learning behaviors mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J. Mater. Chem. C 1(34), 5292–5298 (2013). https://doi.org/10.1039/C3TC30575A
W. Xu, S. Min, H. Hwang, T.W. Lee, Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2(6), e1501326 (2016). https://doi.org/10.1126/sciadv.1501326
W. Xu, T.L. Nguyue, Y. Kim, C. Wolf, R. Pfattner et al., Ultrasensitive artificial synapse based on conjugated polyelectrolyte. Nano Energy 48, 575–581 (2018). https://doi.org/10.1016/j.nanoen.2018.02.058
F. Zeng, S. Li, J. Yang, F. Pan, D. Guo, Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell. RSC Adv. 4(29), 14822–14828 (2014). https://doi.org/10.1039/C3RA46679E
D. Seo, Y. Lee, G. Go, M. Pei, S. Jung et al., Versatile neuromorphic electronics by modulating synaptic decay of single organic synaptic transistor: From artificial neural networks to neuro-prosthetics. Nano Energy 65, 104035 (2019). https://doi.org/10.1016/j.nanoen.2019.104035
G. Go, Y. Lee, D. Seo, M. Pei, W. Lee et al., Achieving microstructure-controlled synaptic plasticity and long-term retention in ion-gel-gated organic synaptic transistors. Adv. Intell. Syst. 2(11), 2000012 (2020). https://doi.org/10.1002/aisy.202000012
J. Tang, F. Yuan, X. Shen, Z. Wang, M. Rao et al., Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31(49), 1902761 (2019). https://doi.org/10.1002/adma.201902761
J.-U. Woo, H.-G. Hwang, S.-M. Park, T.-G. Lee, S. Nahm, Improvement in conductance modulation linearity of artificial synapses based on NaNbO3 memristor. Appl. Mater. Today 19, 100582 (2020). https://doi.org/10.1016/j.apmt.2020.100582
D.-T. Wang, Y.-W. Dai, J. Xu, L. Chen, Q.-Q. Sun et al., Resistive switching and synaptic behaviors of TaN/Al2O3/ZnO/ITO flexible devices with embedded Ag nanoparticles. IEEE Electron Device Lett. 37(7), 16105146 (2016). https://doi.org/10.1109/LED.2016.2570279
S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder et al., Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010). https://doi.org/10.1021/nl904092h
D.J. Kim, H. Lu, S. Ryu, C.W. Bark, C.B. Eom et al., Ferroelectric tunnel memristor. Nano Lett. 12(11), 5697–5702 (2012). https://doi.org/10.1021/nl302912t
S.V. Kalinin, B.J. Rodriguez, A.Y. Borisevich, A.P. Baddorf, N. Balke et al., Defect-mediated polarization switching in ferroelectrics and related materials: from mesoscopic mechanisms to atomistic control. Adv. Mater. 22(3), 314–322 (2010). https://doi.org/10.1002/adma.200900813
A. Chanthbouala, V. Garcia, R.O. Cherifi, K. Bouzehouane, S. Fusil et al., A ferroelectric memristor. Nat. Mater. 11(10), 860–864 (2012). https://doi.org/10.1038/nmat3415
J. Guyonnet, I. Gaponenko, S. Gariglio, P. Paruch, Conduction at domain walls in insulating Pb(Zr0.2 Ti0.8)O3 thin films. Adv. Mater. 23(45), 5377–5382 (2011). https://doi.org/10.1002/adma.201102254
J. Li, C. Ge, J. Du, C. Wang, G. Yang et al., Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing. Adv. Mater. 32(7), 1905764 (2020). https://doi.org/10.1002/adma.201905764
G. Zhong, M. Zi, C. Ren, Q. Xiao, M. Tang et al., Flexible electronic synapse enabled by ferroelectric field effect transistor for robust neuromorphic computing. Appl. Phys. Lett. 117(9), 092903 (2020). https://doi.org/10.1063/5.0013638
Y. Shi, S. Fong, H.-S.P. Wong, D. Kuzum, Synaptic devices based on phase-change memory, in Neuro-inspired Computing Using Resistive Synaptic Devices. ed. by S. Yu (Springer, Berlin, 2017), pp. 19–51. https://doi.org/10.1007/978-3-319-54313-0_2
S.-H. Lee, Y. Jung, A.T. Jennings, R. Agarwal, Core-shell heterostructured phase change nanowire multistate memory. Nano Lett. 8(7), 2056–2062 (2008). https://doi.org/10.1021/nl801482z
N. Yamada, M. Wuttig, Phase change materials for rewriteable data storage. Nat. Mater. 6(11), 824–832 (2007). https://doi.org/10.1038/nmat2009
T. Tuma, A. Pantazi, M.L. Gallo, A. Sebastian, E. Eleftheriou, Stochastic phase-change neurons. Nat. Nanotechnol. 11(8), 693–699 (2016). https://doi.org/10.1038/nnano.2016.70
K. Ren, R. Li, X. Chen, Y. Wang, J. Shen et al., Controllable set process in O-Ti-Sb-Te based phase change memory for synaptic application. Appl. Phys. Lett. 112, 073106 (2018). https://doi.org/10.1063/1.5018513
D. Kuzum, R.G. Jeyasingh, B. Lee, H.S. Wong, Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12(5), 2179–2186 (2012). https://doi.org/10.1021/nl201040y
O. Bichler,M. Suri, D. Querlioz, O. Cueto, L. Perniola et al., Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. IEDM 12504168 (2012). https://doi.org/https://doi.org/10.1109/IEDM.2011.6131488
L.V. Tho, K.J. Baeg, Y.Y. Noh, Organic nano-floating-gate transistor memory with metal nanoparticles. Nano Convergence 3, 10 (2016). https://doi.org/10.1186/s40580-016-0069-7
D. Sarkar, J. Tao, W. Wang, Q. Lin, M. Yeung et al., Mimicking biological synaptic functionality with an indium phosphide synaptic device on silicon for scalable neuromorphic computing. ACS Nano 12(2), 1656–1663 (2018). https://doi.org/10.1021/acsnano.7b08272
M. Zhang, Z. Fan, X. Jiang, H. Zhu, L. Chen et al., MoS2-based charge-trapping synaptic device with electrical and optical modulated conductance. Nanophotonics 9(8), 2475–2486 (2020). https://doi.org/10.1515/nanoph-2019-0548
S.-R. Zhang, L. Zhou, J.-Y. Mao, Y. Ren, J.-Q. Yang et al., Artificial synapse emulated by charge trapping-based resistive switching device. Adv. Mater. Technol. 4(2), 1800342 (2019). https://doi.org/10.1002/admt.201800342
F. Alibart, S. Pleutin, D. Guerin, C. Novembre, S. Lenfant et al., An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater. 20, 330–337 (2010). https://doi.org/10.1002/adfm.200901335
J. Jadwiszczak, D. Keane, P.R. Maguire, C.P. Cullen, H. Zhang et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13(12), 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
C. Liu, H. Chen, S. Wang, H. Zhang, Q. Liu et al., Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15(7), 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3
G.M. Marega, Y. Zhao, A. Avsar, Z. Wang, M. Tripathi et al., Logic-in-memory based on an atomically thin semiconductor. Nature 587, 72–77 (2020). https://doi.org/10.1038/s41586-020-2861-0
V.K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M.E. Beck et al., Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018). https://doi.org/10.1038/nature25747
S. Wang, D.W. Zhang, P. Zhou, Two-dimensional materials for synaptic electronics and neuromorphic systems. Sci. Bull. 64(15), 1056–1066 (2019). https://doi.org/10.1016/j.scib.2019.01.016
Y. Shi, C. Pan, V. Chen, N. Raghavan, K.L. Pey et al., Coexistence of volatile and non-volatile resistive switching in 2D h-BN based electronic synapses. IEDM 17524736 (2018). https://doi.org/https://doi.org/10.1109/IEDM.2017.8268333
Y. Shi, X. Liang, B. Yuan, V. Chen, H. Li et al., Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1(8), 458–465 (2018). https://doi.org/10.1038/s41928-018-0118-9
X. Zhu, D. Li, X. Liang, W.D. Lu, Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18(2), 141–148 (2019). https://doi.org/10.1038/s41563-018-0248-5
S. Seo, B.S. Kang, J.-J. Lee, H.-J. Ryu, S. Kim et al., Artificial van der waals hybrid synapse and its application to acoustic pattern recognition. Nat. Commun. 11, 3936 (2020). https://doi.org/10.1038/s41467-020-17849-3
H. Tian, Q. Guo, Y. Xie, H. Zhao, C. Li et al., Anisotropic black phosphorus synaptic device for neuromorphic applications. Adv. Mater. 28(25), 4991–4997 (2016). https://doi.org/10.1002/adma.201600166
X. Zhu, W.D. Lu, Optogenetics-inspired tunable synaptic functions in memristors. ACS Nano 12(2), 1242–1249 (2018). https://doi.org/10.1021/acsnano.7b07317
S. Seo, S.H. Jo, S. Kim, J. Shim, S. Oh et al., Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat. Commun. 9(1), 5106 (2018). https://doi.org/10.1038/s41467-018-07572-5
M. Lee, W. Lee, S. Choi, J.W. Jo, J. Kim et al., Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semiconductors and their persistent photoconductivity. Adv. Mater. 29(28), 1700951 (2017). https://doi.org/10.1002/adma.201700951
D.C. Hu, R. Yang, L. Jiang, X. Guo, Memristive synapses with photoelectric plasticity realized in ZnO1-x/AlOy heterojunction. ACS Appl. Mater. Interfaces 10(7), 6463–6470 (2018). https://doi.org/10.1021/acsami.8b01036
M. Kumar, S. Abbas, J. Kim, All-oxide-based highly transparent photonic synapse for neuromorphic computing. ACS Appl. Mater. Interfaces 10(40), 34370–34376 (2018). https://doi.org/10.1021/acsami.8b10870
H.K. Li, T.P. Chen, P. Liu, S.G. Hu, Y. Liu et al., A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnOx–Al2O3 thin film structure. J. Appl. Phys. 119, 244505 (2016). https://doi.org/10.1063/1.4955042
S. Song, M. Kim, G. Yoo, S.-M. Kwon, J.-S. Heo et al., Solution-processed oxide semiconductor-based artificial optoelectronic synapse array for spatiotemporal synaptic integration. J. Alloy. Compd. 857, 158027 (2021). https://doi.org/10.1016/j.jallcom.2020.158027
L. Yin, W. Huang, R. Xiao, W. Peng, Y. Zhu et al., Optically stimulated synaptic devices based on the hybrid structure of silicon nanomembrane and perovskite. Nano Lett. 20(5), 3378–3387 (2020). https://doi.org/10.1021/acs.nanolett.0c00298
S. Dai, X. Wu, D. Liu, Y. Chu, K. Wang et al., Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl. Mater. Interfaces 10(25), 21472–21480 (2018). https://doi.org/10.1021/acsami.8b05036
Y. Sun, L. Qian, D. Xie, Y. Lin, M. Sun et al., Photoelectric synaptic plasticity realized by 2D perovskite. Adv. Funct. Mater. 29(28), 1902538 (2019). https://doi.org/10.1002/adfm.201902538
L. Yin, C. Han, Q. Zhang, Z. Ni, S. Zhao et al., Synaptic silicon-nanocrystal phototransistors for neuromorphic computing. Nano Energy 63, 103859 (2019). https://doi.org/10.1016/j.nanoen.2019.103859
S. Qin, F. Wang, Y. Liu, Q. Wan, X. Wang et al., A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater. 4(3), 035022 (2017). https://doi.org/10.1088/2053-1583/aa805e
J. Jiang, W. Hu, D. Xie, J. Yang, J. He et al., 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration. Nanoscale 11(3), 1360–1369 (2019). https://doi.org/10.1039/C8NR07133K
L. Chu, W. Ahmad, W. Liu, J. Yang, R. Zhang et al., Lead-free halide double perovskite materials: a new superstar toward green and stable optoelectronic applications. Nano-Micro Lett. 11, 16 (2019). https://doi.org/10.1007/s40820-019-0244-6
Y. Fang, Q. Dong, Y. Shao, P. Mulligan, J. Qiu et al., Electron-hole diffusion lengths >175 μm in solution-grown CH3NH3PbI3 single crystals. Science 347(6225), 967–970 (2015). https://doi.org/10.1126/science.aaa5760
A. Zavabeti, A. Jannat, L. Zhong, A.A. Haidry, Z. Yao et al., Two-dimensional materials in large-areas: synthesis, properties and applications. Nano-Micro Lett. 12, 66 (2020). https://doi.org/10.1007/s40820-020-0402-x
Z. Ni, X. Pi, S. Zhou, T. Nozaki, B. Grandidier et al., Size-dependent structures and optical absorption of boron-hyperdoped silicon nanocrystals. Adv. Opt. Mater. 4(5), 700–707 (2016). https://doi.org/10.1002/adom.201500706
R. Islam, P.-Y. Chen, W. Wan, H.-Y. Chen, B. Gao et al., Device and materials requirements for neuromorphic computing. J. Phys. D: Appl. Phys. 52, 113001 (2019). https://doi.org/10.1088/1361-6463/aaf784
W. Huang, P. Hang, Y. Wang, K. Wang, S. Han et al., Zero-power optoelectronic synaptic devices. Nano Energy 73, 104790 (2020). https://doi.org/10.1016/j.nanoen.2020.104790
J. Zhang, S. Dai, Y. Zhao, J. Zhang, J. Huang, Recent progress in photonic synapses for neuromorphic systems. Adv. Intell. Syst. 2, 1900136 (2020). https://doi.org/10.1002/aisy.201900136
C. Ríos, Z. Cheng, W.H.P. Pernice, C.D. Wright, H. Bhaskaran, On-chip photonic synapse. Sci. Adv. 3(9), 1700160 (2017). https://doi.org/10.1126/sciadv.1700160
G. Pacchioni, Oxygen vacancy: the invisible agent on oxide surfaces. ChemPhysChem 4(10), 1041–1047 (2003). https://doi.org/10.1002/cphc.200300835
H. Song, G. Kang, Y. Kang, S. Han, the nature of the oxygen vacancy in amorphous oxide semiconductors: shallow versus deep. Phys. Status Solidi 256(3), 1800486 (2019). https://doi.org/10.1002/pssb.201800486
H.-K. Noh, B. Ryu, E.-A. Choi, K.J. Chang, O-vacancy as the origin of negative bias illumination stress instability in amorphous InGaZnO thin film transistors. Appl. Phys. Lett. 97, 022108 (2010). https://doi.org/10.1063/1.3464964
J.J. Yu, L.Y. Liang, L.X. Hu, H.X. Duan, W.H. Wu et al., Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation. Nano Energy 62, 772–780 (2019). https://doi.org/10.1016/j.nanoen.2019.06.007
Q. Wu, J. Wang, J. Cao, C. Lu, G. Yang et al., Photoelectric plasticity in oxide thin film transistors with tunable synaptic functions. Adv. Electron. Mater. 4(12), 1800556 (2018). https://doi.org/10.1002/aelm.201800556
J. Yu, K. Javaid, L. Liang, W. Wu, Y. Liang et al., High-performance visible-blind ultraviolet photodetector based on IGZO TFT coupled with p-n heterojunction. ACS Appl. Mater. Interfaces 10(9), 8102–8109 (2018). https://doi.org/10.1021/acsami.7b16498
H. Tan, G. Liu, X. Zhu, H. Yang, B. Chen et al., An optoelectronic resistive switching memory with integrated demodulating and arithmetic functions. Adv. Mater. 27(17), 2797–2803 (2015). https://doi.org/10.1002/adma.201500039
X. Han, Z. Xu, W. Wu, X. Liu, P. Yan et al., Recent progress in optoelectronic synapses for artificial visual-perception system. Small Struct. 1(3), 2000029 (2020). https://doi.org/10.1002/sstr.202000029
S.F. Leung, K.T. Ho, P.K. Kung, V.K.S. Hsiao, H.N. Alshareef et al., A self-poweredand flexible organometallic halide perovskite photodetector with very high detectivity. Adv. Mater. 3(8), 1704611 (2018). https://doi.org/10.1002/adma.201704611
Y. Ogomi, A. Morita, S. Tsukamoto, T. Saitho, N. Fujikawa et al., CH3NH3SnxPb(1–x)I3 perovskite solar cells covering up to 1060 nm. J. Phys. Chem. Lett. 5(6), 1004–1011 (2014). https://doi.org/10.1021/jz5002117
B.R. Sutherland, A.K. Johnston, A.H. Ip, J. Xu, V. Adinolfi et al., Sensitive, fast, and stable perovskite photodetectors exploiting interface engineering. ACS Photonics 2(8), 1117–1123 (2015). https://doi.org/10.1021/acsphotonics.5b00164
J. Sun, Y. Choi, Y.J. Choi, S. Kim, J.H. Park et al., 2D-organic hybrid heterostructures for optoelectronic applications. Adv. Mater. 31(34), 1803831 (2019). https://doi.org/10.1002/adma.201803831
Q. Zhao, W. Wang, F. Carrascoso-Plana, W. Jie, T. Wang et al., The role of traps in the photocurrent generation mechanism in thin InSe photodetectors. Mater. Horizons 7(1), 252–262 (2020). https://doi.org/10.1039/C9MH01020C
M. Dasog, L.V. Titova, F.A. Hegmann, J.G.C. Veinot, Size vs surface tuning the photoluminescence of freestanding silicon nanocrystals across the visible spectrum via surface groups. ACS Nano 8(9), 9636–9648 (2014). https://doi.org/10.1021/nn504109a
T.H. Han, S. Tan, J. Xue, L. Meng, J.W. Lee et al., Interface and defect engineering for metal halide perovskite optoelectronic devices. Adv. Mater. 31(47), 1803515 (2019). https://doi.org/10.1002/adma.201803515
T.H. Tsai, Z.Y. Liang, Y.C. Lin, C.C. Wang, K.I. Lin et al., Photogating WS2 photodetectors using embedded WSe2 charge puddles. ACS Nano 14(4), 4559–4566 (2020). https://doi.org/10.1021/acsnano.0c00098
L. Qian, Y. Sun, M. Wu, C. Li, D. Xie et al., A lead-free two-dimensional perovskite for a high-performance flexible photoconductor and a light-stimulated synaptic device. Nanoscale 10(15), 6837–6843 (2018). https://doi.org/10.1039/c8nr00914g
B. Li, W. Wei, X. Yan, X. Zhang, P. Liu et al., Mimicking synaptic functionality with an InAs nanowire phototransistor. Nanotechnology 29, 464004 (2018). https://doi.org/10.1088/1361-6528/aadf63
K. Wang, S. Dai, Y. Zhao, Y. Wang, C. Liu et al., Light-stimulated synaptic transistors fabricated by a facile solution process based on inorganic perovskite quantum dots and organic semiconductors. Small 15(11), 1900010 (2019). https://doi.org/10.1002/smll.201900010
S.R. Ovshinsky, Optically induced phase changes in amorphous materials. J. Non-Cryst. Solids. 141, 200–203 (1992). https://doi.org/10.1016/S0022-3093(05)80534-4
C. Ríos, M. Stegmaier, P. Hosseini, D. Wang, T. Scherer et al., Integrated all-photonic non-volatile multi-level memory. Nat. Photonics 9, 725–732 (2015). https://doi.org/10.1038/nphoton.2015.182
C. Qian, S. Oh, Y. Choi, J.-H. Kim, J. Sun et al., Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing. Nano Energy 66, 104095 (2019). https://doi.org/10.1016/j.nanoen.2019.104095
S. Ham, S. Choi, H. Cho, S.-I. Na, G. Wang, Photonic organolead halide perovskite artificial synapse capable of accelerated learning at low power inspired by dopamine-facilitated synaptic activity. Adv. Funct. Mater. 29(5), 1806646 (2019). https://doi.org/10.1002/adfm.201806646
L.K. Ono, E.J. Juarez-Perez, Y. Qi, Progress on perovskite materials and solar cells with mixed cations and halide anions. ACS Appl. Mater. Interfaces 9(36), 30197–30246 (2017). https://doi.org/10.1021/acsami.7b06001
Y. Xie, E. Wu, J. Zhang, H. Zhang, X. Hu et al., Dynamically controllable polarity modulation of MoTe2 field-effect transistors through ultraviolet light and electrostatic activation. Sci. Adv. 5(5), aav3430 (2019). https://doi.org/10.1126/sciadv.aav3430
R. Inoue, S. Ishikawa, R. Imura, Y. Kitanaka, T. Oguchi et al., Giant photovoltaic effect of ferroelectric domain walls in perovskite single crystals. Sci. Rep. 5, 14741 (2015). https://doi.org/10.1038/srep14741
Y. Wang, Z. Lv, J. Chen, Z. Wang, Y. Zhou et al., Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30(38), 1802883 (2018). https://doi.org/10.1002/adma.201802883
S. Wang, C. Chen, Z. Yu, Y. He, X. Chen et al., A MoS2/PTCDA hybrid heterojunction synapse with efficient photoelectric dual modulation and versatility. Adv. Mater. 31(3), 1806227 (2019). https://doi.org/10.1002/adma.201806227
S. Wang, X. Hou, L. Liu, J. Li, Y. Shan et al., A photoelectric-stimulated MoS2 transistor for neuromorphic engineering. Research 2019, 1618798 (2019). https://doi.org/https://doi.org/10.34133/2019/1618798
Y. He, Y. Yang, S. Nie, Y. Shi, Q. Wan, Light stimulated IGZO-based electric-double-layer transistors for photoelectric neuromorphic devices. IEEE Electron Device Lett. 39(6), 897–900 (2018). https://doi.org/10.1109/LED.2018.2824339
J. Sun, S. Oh, Y. Choi, S. Seo, M.J. Oh et al., Optoelectronic synapse based on IGZO-alkylated graphene oxide hybrid structure. Adv. Funct. Mater. 28(47), 1804397 (2018). https://doi.org/10.1002/adfm.201804397
T. Morera-Herreras, Y. Gioanni, S. Perez, G. Vignoud, L. Venance, Environmental enrichment shapes striatal spike-timing-dependent plasticity in vivo. Sci. Rep. 9(1), 19451 (2019). https://doi.org/10.1038/s41598-019-55842-z
R.A. John, N. Yantara, Y.F. Ng, G. Narasimman, E. Mosconi et al., Ionotronic halide perovskite drift-diffusive synapses for low-power neuromorphic computation. Adv. Mater. 30, 1805454 (2018). https://doi.org/10.1002/adma.201805454
D. Przyczyna, M. Lis, K. Pilarczyk, K. Szacilowski, Hardware realization of the pattern recognition with an artificial neuromorphic device exhibiting a short-term memory. Molecules 24(15), 2738 (2019). https://doi.org/10.3390/molecules24152738
F. Alibart, E. Zamanidoost, D.B. Strukov, Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4(1), 2072 (2013). https://doi.org/10.1038/ncomms3072
P. Yao, H. Wu, B. Gao, S.B. Eryilmaz, X. Huang et al., Face classification using electronic synapses. Nat. Commun. 18, 15199 (2017). https://doi.org/10.1038/ncomms15199
M. London, M. Häusser, Dendritic computation. Annu. Rev. Neurosci. 28(1), 503–532 (2005). https://doi.org/10.1146/annurev.neuro.28.061604.135703
D. Hao, J. Zhang, S. Dai, J. Zhang, J. Huang, Perovskite/organic semiconductor-based photonic synaptic transistor for artificial visual system. ACS Appl. Mater. Interfaces 12(35), 39487–39495 (2020). https://doi.org/10.1021/acsami.0c10851
A. Gruart, R. Leal-Campanario, J.C. Lopez-Ramos, J.M. Delgado-Garcia, Functional basis of associative learning and its relationships with long-term potentiation evoked in the involved neural circuits: lessons from studies in behaving mammals. Neurobiol. Learn. Mem. 124, 3–18 (2015). https://doi.org/10.1016/j.nlm.2015.04.006
J. Rushen, Using aversion learning techniques to assess the mental state, suffering, and welfare of farm animals. J. Anim. Sci. 74(8), 1990–1995 (1990). https://doi.org/10.2527/1996.7481990x
R.A. John, F. Liu, N.A. Chien, M.R. Kulkarni, C. Zhu et al., Synergistic gating of electro-iono-photoactive 2D chalcogenide neuristors: coexistence of hebbian and homeostatic synaptic metaplasticity. Adv. Mater. 30(25), 1800220 (2018). https://doi.org/10.1002/adma.201800220
L.Q. Zhu, C.J. Wan, L.Q. Guo, Y. Shi, Q. Wan, Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 5(1), 3158 (2014). https://doi.org/10.1038/ncomms4158
S. Herculano-Houzel, The human brain in numbers: a linearly scaled-up primate brain. Front. Neurosci. 3(31), 31 (2009). https://doi.org/10.3389/neuro.09.031.2009
S. Park, M. Chu, J. Kim, J. Noh, M. Jeon et al., Electronic system with memristive synapses for pattern recognition. Sci. Rep. 5, 10123 (2015). https://doi.org/10.1038/srep10123
J. Zhou, N. Liu, L. Zhu, Y. Shi, Q. Wan, Energy-efficient artificial synapses based on flexible IGZO electric-double-layer transistors. IEEE Electron Device Lett. 36(2), 198–200 (2015). https://doi.org/10.1109/LED.2014.2381631
F. Gül, Addressing the sneak-path problem in crossbar RRAM devices using memristor-based one schottky diode-one resistor array. Results Phys. 12, 1091–1096 (2019). https://doi.org/10.1016/j.rinp.2018.12.092