Issue

Vol. 14 (2022)

Correction to: Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

https://doi.org/10.1007/s40820-022-00816-6
Ki Chang Kwon
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
https://orcid.org/0000-0002-4004-5372
Ji Hyun Baek
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
https://orcid.org/0000-0002-2538-2354
Kootak Hong
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
https://orcid.org/0000-0001-7402-5356
Soo Young Kim
Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul 02841, Republic of Korea
https://orcid.org/0000-0002-0685-7991
Ho Won Jang
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
https://orcid.org/0000-0002-6952-7359

Corresponding Author: Ho Won Jang

hwjang@snu.ac.kr

Nano-Micro Letters, Vol. 14 (2022), Article Number: 71

Abstract

Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.


Highlights:


1 Based on the benefits of two-dimensional (2D) transition metal chalcogenides (TMC) materials, the operating concepts and basics of memristors for neuromorphic computing are introduced.
2 The prospects of 2D TMC materials and heterostructures are reviewed, as well as the state-of-the-art demonstration of 2D TMCs-based memristors for neuromorphic computing applications.
3 The most recent advances, current challenges, and future prospects for the manufacture and characterization of memristive neuromorphic devices based on 2D TMCs are discussed.

Keywords

Two-dimensional materials Memristors Neuromorphic computing Artificial synapses Transition metal chalcogenides
Kwon, Ki Chang, Ji Hyun Baek, Kootak Hong, Soo Young Kim, and Ho Won Jang. 2022. “Correction To: Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing”. Nano-Micro Letters 14 (March):71. https://doi.org/10.1007/s40820-022-00816-6.
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References
  1. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
  2. C.D. James, J.B. Aimone, N.E. Miner, C.M. Vineyard, F.H. Rothganger et al., A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. Biol. Inspired Cogn. Archit. 19, 49–64 (2017). https://doi.org/10.1016/j.bica.2016.11.002
  3. T. Bohnstingl, F. Scherr, C. Pehle, K. Meier, W. Maass, Neuromorphic hardware learns to learn. Front. Neurosci. 13, 483 (2019). https://doi.org/10.3389/fnins.2019.00483
  4. G. Indiveri, E. Chicca, R.J. Douglas, Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cognit. Comput. 1(2), 119–127 (2009). https://doi.org/10.1007/s12559-008-9003-6
  5. G. Indiveri, B. Linares-Barranco, T. Hamilton, A. Schaik, R. Etienne-Cummings et al., Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011). https://doi.org/10.3389/fnins.2011.00073
  6. S. Yu, P.Y. Chen, Emerging memory technologies: recent trends and prospects. IEEE Solid-State Circuits Mag. 8(2), 43–56 (2016). https://doi.org/10.1109/MSSC.2016.2546199
  7. D.S. Jeong, K.M. Kim, S. Kim, B.J. Choi, C.S. Hwang, Memristors for energy-efficient new computing paradigms. Adv. Electron. Mater. 2(9), 1600090 (2016). https://doi.org/10.1002/aelm.201600090
  8. A. Serb, A. Corna, R. George, A. Khiat, F. Rocchi et al., Memristive synapses connect brain and silicon spiking neurons. Sci. Rep. 10(1), 2590 (2020). https://doi.org/10.1038/s41598-020-58831-9
  9. P.A. Merolla, J.V. Arthur, R. Alvarez-Icaza, A.S. Cassidy, J. Sawada et al., A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014). https://doi.org/10.1126/science.1254642
  10. Q. Lu, F. Sun, L. Liu, L. Li, Y. Wang et al., Biological receptor-inspired flexible artificial synapse based on ionic dynamics. Microsyst. Nanoeng. 6(1), 84 (2020). https://doi.org/10.1038/s41378-020-00189-z
  11. E. Bullmore, O. Sporns, The economy of brain network organization. Nat. Rev. Neurosci. 13(5), 336–349 (2012). https://doi.org/10.1038/nrn3214
  12. L. Zhao, B. Beverlin, T. Netoff, D. Nykamp, Synchronization from second order network connectivity statistics. Front. Comput. Neurosci. 5, 28 (2011). https://doi.org/10.3389/fncom.2011.00028
  13. Y. Choi, S. Oh, C. Qian, J.H. Park, J.H. Cho, Vertical organic synapse expandable to 3D crossbar array. Nat. Commun. 11(1), 4595 (2020). https://doi.org/10.1038/s41467-020-17850-w
  14. Ö. Türel, J.H. Lee, X. Ma, K.K. Likharev, Neuromorphic architectures for nanoelectronic circuits. Int. J. Circuit Theory Appl. 32(5), 277–302 (2004)
  15. J.J. Wang, S.G. Hu, X.T. Zhan, Q. Yu, Z. Liu et al., Handwritten-digit recognition by hybrid convolutional neural network based on HfO2 memristive spiking-neuron. Sci. Rep. 8(1), 12546 (2018). https://doi.org/10.1038/s41598-018-30768-0
  16. 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
  17. C. Qian, J. Sun, L. Kong, G. Gou, J. Yang et al., Artificial synapses based on in-plane gate organic electrochemical transistors. ACS Appl. Mater. Interfaces 8(39), 26169–26175 (2016). https://doi.org/10.1021/acsami.6b08866
  18. L. Chua, Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971). https://doi.org/10.1109/TCT.1971.1083337
  19. L.O. Chua, S.M. Kang, Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976). https://doi.org/10.1109/PROC.1976.10092
  20. Z. Xiao, J. Huang, Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2(7), 1600100 (2016). https://doi.org/10.1002/aelm.201600100
  21. G. Rachmuth, H.Z. Shouval, M.F. Bear, C.S. Poon, A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity. Proc. Natl. Acad. Sci. 108(49), E1266–E1274 (2011). https://doi.org/10.1073/pnas.1106161108
  22. 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(8), 591–595 (2011). https://doi.org/10.1038/nmat3054
  23. C. Lee, P. Panda, G. Srinivasan, K. Roy, Training deep spiking convolutional neural networks with STDP-based unsupervised pre-training followed by supervised fine-tuning. Front. Neurosci. 12, 435 (2018). https://doi.org/10.3389/fnins.2018.00435
  24. T. Iakymchuk, A. Rosado-Muñoz, J.F. Guerrero-Martínez, M. Bataller-Mompeán, J.V. Francés-Víllora, Simplified spiking neural network architecture and STDP learning algorithm applied to image classification. EURASIP J. Image Video Process. 2015(1), 4 (2015). https://doi.org/10.1186/s13640-015-0059-4
  25. Y. Li, Y. Zhong, J. Zhang, L. Xu, Q. Wang et al., Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci. Rep. 4, 4906 (2014). https://doi.org/10.1038/srep04906
  26. A.J. Arnold, A. Razavieh, J.R. Nasr, D.S. Schulman, C.M. Eichfeld et al., Mimicking neurotransmitter release in chemical synapses via hysteresis engineering in MoS2 transistors. ACS Nano 11(3), 3110–3118 (2017). https://doi.org/10.1021/acsnano.7b00113
  27. L. Bao, J. Zhu, Z. Yu, R. Jia, Q. Cai et al., Dual-gated MoS2 neuristor for neuromorphic computing. ACS Appl. Mater. Interfaces 11(44), 41482–41489 (2019). https://doi.org/10.1021/acsami.9b10072
  28. Z. Zhang, S. Wang, C. Liu, R. Xie, W. Hu et al., All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition. Nat. Nanotechnol. (2021). https://doi.org/10.1038/s41565-021-01003-1
  29. L. Mennel, J. Symonowicz, S. Wachter, D.K. Polyushkin, A.J. Molina-Mendoza et al., Ultrafast machine vision with 2D material neural network image sensors. Nature 579(7797), 62–66 (2020). https://doi.org/10.1038/s41586-020-2038-x
  30. X. Hou, C. Liu, Y. Ding, L. Liu, S. Wang et al., A logic-memory transistor with the integration of visible information sensing-memory-processing. Adv. Sci. 7(21), 2002072 (2020). https://doi.org/10.1002/advs.202002072
  31. L.R. Squire, Memory and Brain. Oxford University Press (1987).
  32. J.D. Kendall, S. Kumar, The building blocks of a brain-inspired computer. Appl. Phys. Rev. 7(1), 011305 (2020). https://doi.org/10.1063/1.5129306
  33. D. Marković, A. Mizrahi, D. Querlioz, J. Grollier, Physics for neuromorphic computing. Nat. Rev. Phys. 2(9), 499–510 (2020). https://doi.org/10.1038/s42254-020-0208-2
  34. V.M. Ho, J.A. Lee, K.C. Martin, The cell biology of synaptic plasticity. Science 334(6056), 623–628 (2011). https://doi.org/10.1126/science.1209236
  35. D. Kuzum, S. Yu, H.S.P. Wong, Synaptic electronics: materials, devices and applications. Nanotechnology 24(38), 382001 (2013). https://doi.org/10.1088/0957-4484/24/38/382001
  36. R. Stanford, I.M. Ovshinsky, Analog models for information storage and transmission in physiological systems. Mater. Res. Bull. 5(8), 681–690 (1970). https://doi.org/10.1016/0025-5408(70)90109-1
  37. I. Boybat, M.L. Gallo, S.R. Nandakumar, T. Moraitis, T. Parnell et al., Neuromorphic computing with multi-memristive synapses. Nat. Commun. 9(1), 2514 (2018). https://doi.org/10.1038/s41467-018-04933-y
  38. S.J. Martin, P.D. Grimwood, R.G.M. Morris, Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23(1), 649–711 (2000). https://doi.org/10.1146/annurev.neuro.23.1.649
  39. J.C. Magee, C. Grienberger, Synaptic plasticity forms and functions. Annu. Rev. Neurosci. 43(1), 95–117 (2020). https://doi.org/10.1146/annurev-neuro-090919-022842
  40. L.F. Abbott, S.B. Nelson, Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000). https://doi.org/10.1038/81453
  41. N. Caporale, Y. Dan, Spike timing–dependent plasticity: a hebbian learning rule. Annu. Rev. Neurosci. 31(1), 25–46 (2008). https://doi.org/10.1146/annurev.neuro.31.060407.125639
  42. M. Ziegler, C. Riggert, M. Hansen, T. Bartsch, H. Kohlstedt, Memristive hebbian plasticity model: device requirements for the emulation of hebbian plasticity based on memristive devices. IEEE Trans. Biomed. Circuits Syst. 9(2), 197–206 (2015). https://doi.org/10.1109/TBCAS.2015.2410811
  43. S. Choi, S. Jang, J.H. Moon, J.C. Kim, H.Y. Jeong et al., A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems. NPG Asia Mater. 10(12), 1097–1106 (2018). https://doi.org/10.1038/s41427-018-0101-y
  44. K.H. Kim, S. Gaba, D. Wheeler, J.M. Cruz-Albrecht, T. Hussain et al., A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett. 12(1), 389–395 (2012). https://doi.org/10.1021/nl203687n
  45. M. Hu, J.P. Strachan, Z. Li, E.M. Grafals, N. Davila et al., Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication, in Proceedings of the 53rd Annual Design Automation Conference, Association for Computing Machinery, New York, NY, USA, (2016). https://doi.org/10.1145/2897937.2898010
  46. Y. Jeong, W. Lu, Neuromorphic computing using memristor crossbar networks: a focus on bio-inspired approaches. IEEE Nanotechnol. Mag. 12(3), 6–18 (2018). https://doi.org/10.1109/MNANO.2018.2844901
  47. B. Li, S. Li, H. Wang, L. Chen, L. Liu et al., An electronic synapse based on 2D ferroelectric CuInP2S6. Adv. Electron. Mater. 6(12), 2000760 (2020). https://doi.org/10.1002/aelm.202000760
  48. F. Schwierz, Graphene transistors. Nat. Nanotechnol. 5(7), 487–496 (2010). https://doi.org/10.1038/nnano.2010.89
  49. K.S. Novoselov, A.K. Geim, S.V. Morozov, D. Jiang, M.I. Katsnelson et al., Two-dimensional gas of massless Dirac fermions in graphene. Nature 438(7065), 197–200 (2005). https://doi.org/10.1038/nature04233
  50. W. Zhang, Q. Wang, Y. Chen, Z. Wang, A.T.S. Wee, Van der Waals stacked 2D layered materials for optoelectronics. 2D Mater. 3(2), 22001 (2016). https://doi.org/10.1088/2053-1583/3/2/022001
  51. K.S. Novoselov, A. Mishchenko, A. Carvalho, A.H.C. Neto, 2D materials and van der Waals heterostructures. Science 353(6298), 9439 (2016). https://doi.org/10.1126/science.aac9439
  52. Y. Zhang, T.T. Tang, C. Girit, Z. Hao, M.C. Martin et al., Direct observation of a widely tunable bandgap in bilayer graphene. Nature 459(7248), 820–823 (2009). https://doi.org/10.1038/nature08105
  53. L. Li, Y. Yu, G.J. Ye, Q. Ge, X. Ou et al., Black phosphorus field-effect transistors. Nat. Nanotechnol. 9(5), 372–377 (2014). https://doi.org/10.1038/nnano.2014.35
  54. S. Manzeli, D. Ovchinnikov, D. Pasquier, O.V. Yazyev, A. Kis, 2D transition metal dichalcogenides. Nat. Rev. Mater. 2(8), 17033 (2017). https://doi.org/10.1038/natrevmats.2017.33
  55. Z. Hu, Z. Wu, C. Han, J. He, Z. Ni et al., Two-dimensional transition metal dichalcogenides: interface and defect engineering. Chem. Soc. Rev. 47(9), 3100–3128 (2018). https://doi.org/10.1039/C8CS00024G
  56. D. Jariwala, V.K. Sangwan, L.J. Lauhon, T.J. Marks, M.C. Hersam, Emerging device applications for semiconducting two-dimensional transition metal dichalcogenides. ACS Nano 8(2), 1102–1120 (2014). https://doi.org/10.1021/nn500064s
  57. M. Chhowalla, H.S. Shin, G. Eda, L.J. Li, K.P. Loh et al., The chemistry of two-dimensional layered transition metal dichalcogenide nanosheets. Nat. Chem. 5(4), 263–275 (2013). https://doi.org/10.1038/nchem.1589
  58. 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
  59. F. Zhang, H. Zhang, S. Krylyuk, C.A. Milligan, Y. Zhu et al., Electric-field induced structural transition in vertical MoTe2- and Mo1–xWxTe2-based resistive memories. Nat. Mater. 18(1), 55–61 (2019). https://doi.org/10.1038/s41563-018-0234-y
  60. X. Tao, Y. Gu, Crystalline–crystalline phase transformation in two-dimensional In2Se3 thin layers. Nano Lett. 13(8), 3501–3505 (2013). https://doi.org/10.1021/nl400888p
  61. M. Soleimani, M. Pourfath, Ferroelectricity and phase transitions in In2Se3 van der Waals material. Nanoscale 12(44), 22688–22697 (2020). https://doi.org/10.1039/D0NR04096G
  62. 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
  63. M. Wang, S. Cai, C. Pan, C. Wang, X. Lian et al., Robust memristors based on layered two-dimensional materials. Nat. Electron. 1(2), 130–136 (2018). https://doi.org/10.1038/s41928-018-0021-4
  64. R. Ge, X. Wu, M. Kim, J. Shi, S. Sonde et al., Atomristor: nonvolatile resistance switching in atomic sheets of transition metal dichalcogenides. Nano Lett. 18(1), 434–441 (2018). https://doi.org/10.1021/acs.nanolett.7b04342
  65. Q. Zhao, Z. Xie, Y.P. Peng, K. Wang, H. Wang et al., Current status and prospects of memristors based on novel 2D materials. Mater. Horizons. 7(6), 1495–1518 (2020). https://doi.org/10.1039/c9mh02033k
  66. H. Li, L. Tao, J.B. Xu, Intrinsic memristive mechanisms in 2D layered materials for high-performance memory. J. Appl. Phys. 129(5), 50902 (2021). https://doi.org/10.1063/5.0035764
  67. R. Ge, X. Wu, L. Liang, S.M. Hus, Y. Gu et al., A library of atomically thin 2D materials featuring the conductive-point resistive switching phenomenon. Adv. Mater. 33(7), 2007792 (2021). https://doi.org/10.1002/adma.202007792
  68. H.K. He, F.F. Yang, R. Yang, Flexible full two-dimensional memristive synapses of graphene/WSe2−xOy/graphene. Phys. Chem. Chem. Phys. 22(36), 20658–20664 (2020). https://doi.org/10.1039/D0CP03822A
  69. T.J. Ko, M. Wang, C. Yoo, E. Okogbue, M.A. Islam et al., Large-area 2D TMD layers for mechanically reconfigurable electronic devices. J. Phys. D. Appl. Phys. 53(31), 313002 (2020). https://doi.org/10.1088/1361-6463/ab87bb
  70. J.L. Meng, T.Y. Wang, L. Chen, Q.Q. Sun, H. Zhu et al., Energy-efficient flexible photoelectric device with 2D/0D hybrid structure for bio-inspired artificial heterosynapse application. Nano Energy 83, 105815 (2021). https://doi.org/10.1016/j.nanoen.2021.105815
  71. P. Ajayan, P. Kim, K. Banerjee, Two-dimensional van der Waals materials. Phys. Today 69(9), 38–44 (2016). https://doi.org/10.1063/PT.3.3297
  72. L. Sun, Y. Zhang, G. Han, G. Hwang, J. Jiang et al., Self-selective van der Waals heterostructures for large scale memory array. Nat. Commun. 10(1), 3161 (2019). https://doi.org/10.1038/s41467-019-11187-9
  73. E. Lee, S.G. Lee, W.H. Lee, H.C. Lee, N.N. Nguyen et al., Direct CVD growth of a graphene/MoS2 heterostructure with interfacial bonding for two-dimensional electronics. Chem. Mater. 32(11), 4544–4552 (2020). https://doi.org/10.1021/acs.chemmater.0c00503
  74. Z. Huang, W. Han, H. Tang, L. Ren, D.S. Chander et al., Photoelectrochemical-type sunlight photodetector based on MoS2/graphene heterostructure. 2D Mater. 2(3), 35011 (2015). https://doi.org/10.1088/2053-1583/2/3/035011
  75. Y. Ren, L. Hu, J.Y. Mao, J. Yuan, Y.J. Zeng et al., Phosphorene nano-heterostructure based memristors with broadband response synaptic plasticity. J. Mater. Chem. C 6(35), 9383–9393 (2018). https://doi.org/10.1039/C8TC03089H
  76. W. Zhang, H. Gao, C. Deng, T. Lv, S. Hu et al., An ultrathin memristor based on a two-dimensional WS2/MoS2 heterojunction. Nanoscale 13(26), 11497–11504 (2021). https://doi.org/10.1039/D1NR01683K
  77. Z. Lin, B.R. Carvalho, E. Kahn, R. Lv, R. Rao et al., Defect engineering of two-dimensional transition metal dichalcogenides. 2D Mater. 3(2), 22002 (2016). https://doi.org/10.1088/2053-1583/3/2/022002
  78. Q. Liang, Q. Zhang, X. Zhao, M. Liu, A.T.S. Wee, Defect engineering of two-dimensional transition-metal dichalcogenides: applications, challenges, and opportunities. ACS Nano 15(2), 2165–2181 (2021). https://doi.org/10.1021/acsnano.0c09666
  79. Y.C. Lin, D.O. Dumcenco, Y.S. Huang, K. Suenaga, Atomic mechanism of the semiconducting-to-metallic phase transition in single-layered MoS2. Nat. Nanotechnol. 9(5), 391–396 (2014). https://doi.org/10.1038/nnano.2014.64
  80. Y. Cheng, A. Nie, Q. Zhang, L.Y. Gan, R. Shahbazian-Yassar et al., Origin of the phase transition in lithiated molybdenum disulfide. ACS Nano 8(11), 11447–11453 (2014). https://doi.org/10.1021/nn505668c
  81. J. Huang, X. Pan, X. Liao, M. Yan, B. Dunn et al., In situ monitoring of the electrochemically induced phase transition of thermodynamically metastable 1T-MoS2 at nanoscale. Nanoscale 12(16), 9246–9254 (2020). https://doi.org/10.1039/D0NR02161J
  82. K. Zhang, B.M. Bersch, J. Joshi, R. Addou, C.R. Cormier et al., Tuning the electronic and photonic properties of monolayer MoS2 via in situ rhenium substitutional doping. Adv. Funct. Mater. 28(16), 1706950 (2018). https://doi.org/10.1002/adfm.201706950
  83. X. Li, A.A. Puretzky, X. Sang, S. KC, M. Tian, et al., Suppression of defects and deep levels using isoelectronic tungsten substitution in monolayer MoSe2. Adv. Funct. Mater. 27(19), 1650 (2017). https://doi.org/10.1002/adfm.201770119
  84. X. Li, M.W. Lin, L. Basile, S.M. Hus, A.A. Puretzky et al., Isoelectronic tungsten doping in monolayer MoSe2 for carrier type modulation. Adv. Mater. 28(37), 8240–8247 (2016). https://doi.org/10.1002/adma.201601991
  85. A.Y. Lu, H. Zhu, J. Xiao, C.P. Chuu, Y. Han et al., Janus monolayers of transition metal dichalcogenides. Nat. Nanotechnol. 12(8), 744–749 (2017). https://doi.org/10.1038/nnano.2017.100
  86. C. Cui, F. Xue, W.J. Hu, L.J. Li, Two-dimensional materials with piezoelectric and ferroelectric functionalities. NPJ D Mater. Appl. 2(1), 18 (2018). https://doi.org/10.1038/s41699-018-0063-5
  87. K.A.N. Duerloo, M.T. Ong, E.J. Reed, Intrinsic piezoelectricity in two-dimensional materials. J. Phys. Chem. Lett. 3(19), 2871–2876 (2012). https://doi.org/10.1021/jz3012436
  88. A.V. Bune, V.M. Fridkin, S. Ducharme, L.M. Blinov, S.P. Palto et al., Two-dimensional ferroelectric films. Nature 391(6670), 874–877 (1998). https://doi.org/10.1038/36069
  89. 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, 5106 (2018). https://doi.org/10.1038/s41467-018-07572-5
  90. R. Fei, W. Li, J. Li, L. Yang, Giant piezoelectricity of monolayer group IV monochalcogenides: SnSe, SnS, GeSe, and GeS. Appl. Phys. Lett. 107(17), 173104 (2015). https://doi.org/10.1063/1.4934750
  91. D.S. Jeong, R. Thomas, R.S. Katiyar, J.F. Scott, H. Kohlstedt et al., Emerging memories: resistive switching mechanisms and current status. Rep. Prog. Phys. 75(7), 76502 (2012). https://doi.org/10.1088/0034-4885/75/7/076502
  92. S. Oh, T. Kim, M. Kwak, J. Song, J. Woo et al., HfZrOx-based ferroelectric synapse device with 32 levels of conductance states for neuromorphic applications. IEEE Electron Device Lett. 38(6), 732–735 (2017). https://doi.org/10.1109/LED.2017.2698083
  93. Y. Zhang, L. Wang, H. Chen, T. Ma, X. Lu et al., Analog and digital mode α-In2Se3 memristive devices for neuromorphic and memory applications. Adv. Electron. Mater. 2100609 (2021). https://doi.org/10.1002/aelm.202100609
  94. M. Osada, T. Sasaki, The rise of 2D dielectrics/ferroelectrics. APL Mater. 7(12), 120902 (2019). https://doi.org/10.1063/1.5129447
  95. T. Horiuchi, M. Takahashi, K. Ohhashi, S. Sakai, Memory window widening of Pt/SrBi2Ta2O9/HfO2/Si ferroelectric-gate field-effect transistors by nitriding Si. Semicond. Sci. Technol. 24(10), 105026 (2009). https://doi.org/10.1088/0268-1242/24/10/105026
  96. T. Fukushima, T. Yoshimura, K. Masuko, K. Maeda, A. Ashida et al., Electrical characteristics of controlled-polarization-type ferroelectric-gate field-effect transistor. Japaneses J. Appl. Phys. 47(12), 8874–8879 (2008). https://doi.org/10.1143/jjap.47.8874
  97. S.N. Shirodkar, U.V. Waghmare, Emergence of ferroelectricity at a metal-semiconductor transition in a 1T monolayer of MoS2. Phys. Rev. Lett. 112(15), 157601 (2014). https://doi.org/10.1103/PhysRevLett.112.157601
  98. G.B. Liu, D. Xiao, Y. Yao, X. Xu, W. Yao, Electronic structures and theoretical modelling of two-dimensional group-VIB transition metal dichalcogenides. Chem. Soc. Rev. 44(9), 2643–2663 (2015). https://doi.org/10.1039/C4CS00301B
  99. L. Qi, S. Ruan, Y.J. Zeng, Review on recent developments in 2D ferroelectrics: Theories and applications. Adv. Mater. 33(13), 2005098 (2021). https://doi.org/10.1002/adma.202005098
  100. H. Qiao, C. Wang, W.S. Choi, M.H. Park, Y. Kim, Ultra-thin ferroelectrics. Mater. Sci. Eng. R Rep. 145, 100622 (2021). https://doi.org/10.1016/j.mser.2021.100622
  101. F. Liu, L. You, K.L. Seyler, X. Li, P. Yu et al., Room-temperature ferroelectricity in CuInP2S6 ultrathin flakes. Nat. Commun. 7, 12357 (2016). https://doi.org/10.1038/ncomms12357
  102. C. Cui, W.J. Hu, X. Yan, C. Addiego, W. Gao et al., Intercorrelated in-plane and out-of-plane ferroelectricity in ultrathin two-dimensional layered semiconductor In2Se3. Nano Lett. 18(2), 1253–1258 (2018). https://doi.org/10.1021/acs.nanolett.7b04852
  103. M. Wu, S. Dong, K. Yao, J. Liu, X.C. Zeng, Ferroelectricity in covalently functionalized two-dimensional materials: integration of high-mobility semiconductors and nonvolatile memory. Nano Lett. 16(11), 7309–7315 (2016). https://doi.org/10.1021/acs.nanolett.6b04309
  104. J.H. Lee, J.Y. Park, E.B. Cho, T.Y. Kim, S.A. Han et al., Reliable piezoelectricity in bilayer WSe2 for piezoelectric nanogenerators. Adv. Mater. 29(29), 1606667 (2017). https://doi.org/10.1002/adma.201606667
  105. S. Wan, Y. Li, W. Li, X. Mao, W. Zhu et al., Room-temperature ferroelectricity and a switchable diode effect in two-dimensional α-In2Se3 thin layers. Nanoscale 10(31), 14885–14892 (2018). https://doi.org/10.1039/C8NR04422H
  106. G.R. Bhimanapati, Z. Lin, V. Meunier, Y. Jung, J. Cha et al., Recent advances in two-dimensional materials beyond graphene. ACS Nano 9(12), 11509–11539 (2015). https://doi.org/10.1021/acsnano.5b05556
  107. R. Xu, H. Jang, M.H. Lee, D. Amanov, Y. Cho et al., Vertical MoS2 double-layer memristor with electrochemical metallization as an atomic-scale synapse with switching thresholds approaching 100 mV. Nano Lett. 19(4), 2411–2417 (2019). https://doi.org/10.1021/acs.nanolett.8b05140
  108. K. Wang, L. Li, R. Zhao, J. Zhao, Z. Zhou et al., A pure 2H-MoS2 nanosheet-based memristor with low power consumption and linear multilevel storage for artificial synapse emulator. Adv. Electron. Mater. 6(3), 1901342 (2020). https://doi.org/10.1002/aelm.201901342
  109. D. Dev, A. Krishnaprasad, M.S. Shawkat, Z. He, S. Das et al., 2D MoS2-based threshold switching memristor for artificial neuron. IEEE Electron Device Lett. 41(6), 936–939 (2020). https://doi.org/10.1109/LED.2020.2988247
  110. T.J. Ko, H. Li, S.A. Mofid, C. Yoo, E. Okogbue et al., Two-dimensional near-atom-thickness materials for emerging neuromorphic devices and applications. IScience 23(11), 101676 (2020). https://doi.org/10.1016/j.isci.2020.101676
  111. X. Yan, C. Qin, C. Lu, J. Zhao, R. Zhao et al., Robust Ag/ZrO2/WS2/Pt memristor for neuromorphic computing. ACS Appl. Mater. Interfaces 11(51), 48029–48038 (2019). https://doi.org/10.1021/acsami.9b17160
  112. X. Yan, Q. Zhao, A.P. Chen, J. Zhao, Z. Zhou et al., Vacancy-induced synaptic behavior in 2D WS2 nanosheet–based memristor for low-power neuromorphic computing. Small 15(24), 1901423 (2019). https://doi.org/10.1002/smll.201901423
  113. W. Huh, S. Jang, J.Y. Lee, D. Lee, D. Lee et al., Synaptic barristor based on phase-engineered 2D heterostructures. Adv. Mater. 30(35), 1801447 (2018). https://doi.org/10.1002/adma.201801447
  114. A.A. Bessonov, M.N. Kirikova, D.I. Petukhov, M. Allen, T. Ryhänen et al., Layered memristive and memcapacitive switches for printable electronics. Nat. Mater. 14(2), 199–204 (2015). https://doi.org/10.1038/nmat4135
  115. P. Cheng, K. Sun, Y.H. Hu, Memristive behavior and ideal memristor of 1T phase MoS2 nanosheets. Nano Lett. 16(1), 572–576 (2016). https://doi.org/10.1021/acs.nanolett.5b04260
  116. H. Chen, C. Liu, Z. Wu, Y. He, Z. Wang et al., Time-tailoring van der Waals heterostructures for human memory system programming. Adv. Sci. 6(20), 1901072 (2019). https://doi.org/10.1002/advs.201901072
  117. T. Paul, T. Ahmed, K.K. Tiwari, C.S. Thakur, A. Ghosh, A high-performance MoS2 synaptic device with floating gate engineering for neuromorphic computing. 2D Mater. 6(4), 45008 (2019). https://doi.org/10.1088/2053-1583/ab23ba
  118. A. Sawa, Resistive switching in transition metal oxides. Mater. Today 11(6), 28–36 (2008). https://doi.org/10.1016/S1369-7021(08)70119-6
  119. R. Waser, M. Aono, Nanoionics-based resistive switching memories. Nanosci. Technol. 158–165 (2009). https://doi.org/10.1142/9789814287005_0016
  120. R. Waser, R. Dittmann, C. 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
  121. Z. Wang, S. Joshi, S. Savel’Ev, W. Song, R. Midya et al., Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1(2), 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
  122. H. Lim, H.W. Ahn, V. Kornijcuk, G. Kim, J.Y. Seok et al., Relaxation oscillator-realized artificial electronic neurons, their responses, and noise. Nanoscale 8(18), 9629–9640 (2016). https://doi.org/10.1039/c6nr01278g
  123. S. Kc, R.C. Longo, R. Addou, R.M. Wallace, K. Cho, Impact of intrinsic atomic defects on the electronic structure of MoS2 monolayers. Nanotechnology 25(37), 375703 (2014). https://doi.org/10.1088/0957-4484/25/37/375703
  124. W. Zhou, X. Zou, S. Najmaei, Z. Liu, Y. Shi et al., Intrinsic structural defects in monolayer molybdenum disulfide. Nano Lett. 13(6), 2615–2622 (2013). https://doi.org/10.1021/nl4007479
  125. J. Lin, S.T. Pantelides, W. Zhou, Vacancy-induced formation and growth of inversion domains in transition-metal dichalcogenide monolayer. ACS Nano 9(5), 5189–5197 (2015). https://doi.org/10.1021/acsnano.5b00554
  126. G. Gao, Y. Jiao, F. Ma, Y. Jiao, E. Waclawik et al., Charge mediated semiconducting-to-metallic phase transition in molybdenum disulfide monolayer and hydrogen evolution reaction in new 1T′ phase. J. Phys. Chem. C 119(23), 13124–13128 (2015). https://doi.org/10.1021/acs.jpcc.5b04658
  127. X. Fan, P. Xu, D. Zhou, Y. Sun, Y.C. Li et al., Fast and efficient preparation of exfoliated 2H MoS2 nanosheets by sonication-assisted lithium intercalation and infrared laser-induced 1T to 2H phase reversion. Nano Lett. 15(9), 5956–5960 (2015). https://doi.org/10.1021/acs.nanolett.5b02091
  128. M. Kan, J.Y. Wang, X.W. Li, S.H. Zhang, Y.W. Li et al., Structures and phase transition of a MoS2 monolayer. J. Phys. Chem. C 118(3), 1515–1522 (2014). https://doi.org/10.1021/jp4076355
  129. M. Yoshida, R. Suzuki, Y. Zhang, M. Nakano, Y. Iwasa, Memristive phase switching in two-dimensional 1T-TaS2 crystals. Sci. Adv. 1(9), 1500606 (2015). https://doi.org/10.1126/sciadv.1500606
  130. M. Acerce, D. Voiry, M. Chhowalla, Metallic 1T phase MoS2 nanosheets as supercapacitor electrode materials. Nat. Nanotechnol. 10(4), 313–318 (2015). https://doi.org/10.1038/nnano.2015.40
  131. Y.C. King, T.J. King, C. Hu, Charge-trap memory device fabricated by oxidation of Si1-xGex. IEEE Trans. Electron Devices. 48(4), 696–700 (2001). https://doi.org/10.1109/16.915694
  132. H.W. You, W.J. Cho, Charge trapping properties of the HfO2 layer with various thicknesses for charge trap flash memory applications. Appl. Phys. Lett. 96(9), 3–5 (2010). https://doi.org/10.1063/1.3337103
  133. A. Sawa, T. Fujii, M. Kawasaki, Y. Tokura, Interface resistance switching at a few nanometer thick perovskite manganite active layers. Appl. Phys. Lett. 88(23), 86–89 (2006). https://doi.org/10.1063/1.2211147
  134. S.H. Kim, S.G. Yi, M.U. Park, C. Lee, M. Kim et al., Multilevel MoS2 optical memory with photoresponsive top floating gates. ACS Appl. Mater. Interfaces 11(28), 25306–25312 (2019). https://doi.org/10.1021/acsami.9b05491
  135. E. Zhang, W. Wang, C. Zhang, Y. Jin, G. Zhu et al., Tunable charge-trap memory based on few-layer MoS2. ACS Nano 9(1), 612–619 (2015). https://doi.org/10.1021/nn5059419
  136. Q.A. Vu, Y.S. Shin, Y.R. Kim, V.L. Nguyen, W.T. Kang et al., Two-terminal floating-gate memory with van der Waals heterostructures for ultrahigh on/off ratio. Nat. Commun. 7, 12725 (2016). https://doi.org/10.1038/ncomms12725
  137. K. A. Khair, S. S. Ahmed. Strain-dependent polar optical phonon scattering and drive current optimization in nanoscale monolayer MoS2 FETs. Electron. Mater. Lett. 16, 299–309 (2020). https://doi.org/10.1007/s13391-020-00214-3
  138. Z. Xu, F. Li, C. Wu, F. Ma, Y. Zheng et al., Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing. NPG Asia Mater. 11(1), 18 (2019). https://doi.org/10.1038/s41427-019-0118-x
  139. S. Chen, J. Huang, Recent advances in synaptic devices based on halide perovskite. ACS Appl. Electron. Mater. 2(7), 1815–1825 (2020). https://doi.org/10.1021/acsaelm.0c00180
  140. X.F. Lu, Y. Zhang, N. Wang, S. Luo, K. Peng et al., Exploring low power and ultrafast memristor on p-type van der Waals SnS. Nano Lett. 21(20), 8800–8807 (2021). https://doi.org/10.1021/acs.nanolett.1c03169
  141. G. Cao, P. Meng, J. Chen, H. Liu, R. Bian et al., 2D material based synaptic devices for neuromorphic computing. Adv. Funct. Mater. 31(4), 2005443 (2021). https://doi.org/10.1002/adfm.202005443
  142. L. Zhang, T. Gong, H. Wang, Z. Guo, H. Zhang, Memristive devices based on emerging two-dimensional materials beyond graphene. Nanoscale 11(26), 12413–12435 (2019). https://doi.org/10.1039/C9NR02886B
  143. M. Lübben, I. Valov, Active electrode redox reactions and device behavior in ECM type resistive switching memories. Adv. Electron. Mater. 5(9), 1800933 (2019). https://doi.org/10.1002/aelm.201800933
  144. X. Wu, R. Ge, P.A. Chen, H. Chou, Z. Zhang et al., Thinnest nonvolatile memory based on monolayer h-BN. Adv. Mater. 31(15), 1806790 (2019). https://doi.org/10.1002/adma.201806790
  145. H.Q. Yang, X.Y. Wang, H. Wu, B. Zhang, D.D. Xie et al., Sn vacancy engineering for enhancing the thermoelectric performance of two-dimensional SnS. J. Mater. Chem. C 7(11), 3351–3359 (2019). https://doi.org/10.1039/C8TC05711G
  146. K.D. Miller, Synaptic economics: Competition and cooperation in synaptic plasticity. Neuron 17(3), 371–374 (1996). https://doi.org/10.1016/S0896-6273(00)80169-5
  147. M. Chistiakova, N.M. Bannon, M. Bazhenov, M. Volgushev, Heterosynaptic plasticity: multiple mechanisms and multiple roles. Neuroscientist 20(5), 483–498 (2014). https://doi.org/10.1177/1073858414529829
  148. G. Perea, M. Navarrete, A. Araque, Tripartite synapses: astrocytes process and control synaptic information. Trends Neurosci. 32(8), 421–431 (2009). https://doi.org/10.1016/j.tins.2009.05.001
  149. M. Kumar, D.K. Ban, S.M. Kim, J. Kim, C.P. Wong, Vertically aligned WS2 layers for high-performing memristors and artificial synapses. Adv. Electron. Mater. 5(10), 1900467 (2019). https://doi.org/10.1002/aelm.201900467
  150. M.S. Choi, G.H. Lee, Y.J. Yu, D.Y. Lee, S. Hwan Lee et al., Controlled charge trapping by molybdenum disulphide and graphene in ultrathin heterostructured memory devices. Nat. Commun. 4, 1624 (2013). https://doi.org/10.1038/ncomms2652
  151. P.E. Schulz, E.P. Cook, D. Johnston, Changes in paired-pulse facilitation suggest presynaptic involvement in long-term potentiation. J. Neurosci. 14(9), 5325–5337 (1994). https://doi.org/10.1523/JNEUROSCI.14-09-05325.1994
  152. D. Debanne, N.C. Guérineau, B.H. Gähwiler, S.M. Thompson, Paired-pulse facilitation and depression at unitary synapses in rat hippocampus: quantal fluctuation affects subsequent release. J. Physiol. 491(1), 163–176 (1996). https://doi.org/10.1113/jphysiol.1996.sp021204
  153. F.I.M. Craik, J.M. Jennings, Human Memory (Handb. Aging Cogn, Lawrence Erlbaum Associates, 1992), pp. 51–110
  154. R.T. Zacks, L. Hasher, K.Z.H. Li, Human Memory. Handb. Aging Cogn. 2nd Ed., Lawrence Erlbaum Associates, 293–357 (2020).
  155. 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
  156. 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
  157. X. Feng, X. Liu, K.W. Ang, 2D photonic memristor beyond graphene: progress and prospects. Nanophotonics 9(7), 1579–1599 (2020). https://doi.org/10.1515/nanoph-2019-0543
  158. J. Yu, X. Yang, G. Gao, Y. Xiong, Y. Wang et al., Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure. Sci. Adv. 7(12), eabd9117 (2021). https://doi.org/10.1126/sciadv.abd9117
  159. C. Choi, J. Leem, M.S. Kim, A. Taqieddin, C. Cho et al., Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system. Nat. Commun. 11, 5934 (2020). https://doi.org/10.1038/s41467-020-19806-6
  160. S. Yin, C. Song, Y. Sun, L. Qiao, B. Wang et al., Electric and light dual-gate tunable MoS2 memtransistor. ACS Appl. Mater. Interfaces 11(46), 43344–43350 (2019). https://doi.org/10.1021/acsami.9b14259
  161. W. Wang, S. Gao, Y. Li, W. Yue, H. Kan et al., Artificial optoelectronic synapses based on TiNxO2–x/MoS2 heterojunction for neuromorphic computing and visual system. Adv. Funct. Mater. 31(34), 2101201 (2021). https://doi.org/10.1002/adfm.202101201
  162. 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
  163. Z.D. Luo, X. Xia, M.M. Yang, N.R. Wilson, A. Gruverman et al., Artificial optoelectronic synapses based on ferroelectric field-effect enabled 2D transition metal dichalcogenide memristive transistors. ACS Nano 14(1), 746–754 (2020). https://doi.org/10.1021/acsnano.9b07687
  164. 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(15), 1800079 (2018). https://doi.org/10.1002/smll.201800079
  165. H. Qiu, T. Xu, Z. Wang, W. Ren, H. Nan et al., Hopping transport through defect-induced localized states in molybdenum disulphide. Nat. Commun. 4, 2642 (2013). https://doi.org/10.1038/ncomms3642
  166. 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
  167. Y. Guo, X. Wei, J. Shu, B. Liu, J. Yin et al., Charge trapping at the MoS2-SiO2 interface and its effects on the characteristics of MoS2 metal-oxide-semiconductor field effect transistors. Appl. Phys. Lett. 106(10), 103109 (2015). https://doi.org/10.1063/1.4914968
  168. Y. Park, H.W. Baac, J. Heo, G. Yoo, Thermally activated trap charges responsible for hysteresis in multilayer MoS2 field-effect transistors. Appl. Phys. Lett. 108(8), 83102 (2016). https://doi.org/10.1063/1.4942406
  169. F. Ma, Y. Zhu, Z. Xu, Y. Liu, X. Zheng et al., Optoelectronic perovskite synapses for neuromorphic computing. Adv. Funct. Mater. 30(11), 1908901 (2020). https://doi.org/10.1002/adfm.201908901
  170. Y. Wang, J. Yang, Z. Wang, J. Chen, Q. Yang et al., Near-infrared annihilation of conductive filaments in quasiplane MoSe2/Bi2Se3 nanosheets for mimicking heterosynaptic plasticity. Small 15(7), 1805431 (2019). https://doi.org/10.1002/smll.201805431
  171. 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
  172. L. Yang, M. Singh, S.W. Shen, K.Y. Chih, S.W. Liu et al., Transparent and flexible inorganic perovskite photonic artificial synapses with dual-mode operation. Adv. Funct. Mater. 31(6), 2008259 (2021). https://doi.org/10.1002/adfm.202008259
  173. K. Chang, J. Liu, H. Lin, N. Wang, K. Zhao et al., Discovery of robust in-plane ferroelectricity in atomic-thick SnTe. Science 353(6296), 274–278 (2016). https://doi.org/10.1126/science.aad8609
  174. W. Ding, J. Zhu, Z. Wang, Y. Gao, D. Xiao et al., Prediction of intrinsic two-dimensional ferroelectrics in In2Se3 and other III2-VI3 van der Waals materials. Nat. Commun. 8, 14956 (2017). https://doi.org/10.1038/ncomms14956
  175. J. Chu, Y. Wang, X. Wang, K. Hu, G. Rao et al., 2D polarized materials: ferromagnetic, ferrovalley, ferroelectric materials, and related heterostructures. Adv. Mater. 33(5), 2004469 (2021). https://doi.org/10.1002/adma.202004469
  176. J. Guyonnet, H. Béa, P. Paruch, Lateral piezoelectric response across ferroelectric domain walls in thin films. J. Appl. Phys. 108(4), 42002 (2010). https://doi.org/10.1063/1.3474953
  177. K. Asadi, Resistance switching in two-terminal ferroelectric-semiconductor lateral heterostructures. Appl. Phys. Rev. 7(2), 21307 (2020). https://doi.org/10.1063/1.5128611
  178. L.W. Martin, A.M. Rappe, Thin-film ferroelectric materials and their applications. Nat. Rev. Mater. 2(2), 16087 (2016). https://doi.org/10.1038/natrevmats.2016.87
  179. K.C. Kwon, Y. Zhang, L. Wang, W. Yu, X. Wang et al., In-plane ferroelectric tin monosulfide and its application in a ferroelectric analog synaptic device. ACS Nano 14(6), 7628–7638 (2020). https://doi.org/10.1021/acsnano.0c03869
  180. S. Boyn, J. Grollier, G. Lecerf, B. Xu, N. Locatelli et al., Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017). https://doi.org/10.1038/ncomms14736
  181. M.K. Kim, J.S. Lee, Ferroelectric analog synaptic transistors. Nano Lett. 19(3), 2044–2050 (2019). https://doi.org/10.1021/acs.nanolett.9b00180
  182. F. Xue, X. He, W. Liu, D. Periyanagounder, C. Zhang et al., Optoelectronic ferroelectric domain-wall memories made from a single van der Waals ferroelectric. Adv. Funct. Mater. 30(52), 2004206 (2020). https://doi.org/10.1002/adfm.202004206
  183. P.Y. Chen, X. Peng, S. Yu, NeuroSim: a circuit-level macro model for benchmarking neuro-inspired architectures in online learning. IEEE Trans. Comput. Des. Integr. Circuits Syst. 37(12), 3067–3080 (2018). https://doi.org/10.1109/TCAD.2018.2789723
  184. 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(8), 776–782 (2019). https://doi.org/10.1038/s41565-019-0501-3
  185. 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
  186. C. Liu, H. Chen, S. Wang, Q. Liu, Y.G. Jiang 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
  187. M. Chhowalla, D. Jena, H. Zhang, Two-dimensional semiconductors for transistors. Nat. Rev. Mater. 1(11), 16052 (2016). https://doi.org/10.1038/natrevmats.2016.52
  188. X. Cui, G.H. Lee, Y.D. Kim, G. Arefe, P.Y. Huang et al., Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform. Nat. Nanotechnol. 10(6), 534–540 (2015). https://doi.org/10.1038/nnano.2015.70
  189. W. Zhu, V. Perebeinos, M. Freitag, P. Avouris, Carrier scattering, mobilities, and electrostatic potential in monolayer, bilayer, and trilayer graphene. Phys. Rev. B 80(23), 235402 (2009). https://doi.org/10.1103/PhysRevB.80.235402
  190. C. Liu, H. Chen, X. Hou, H. Zhang, J. Han et al., Small footprint transistor architecture for photoswitching logic and in situ memory. Nat. Nanotechnol. 14(7), 662–667 (2019). https://doi.org/10.1038/s41565-019-0462-6
  191. D. Li, B. Wu, X. Zhu, J. Wang, B. Ryu et al., MoS2 memristors exhibiting variable switching characteristics toward biorealistic synaptic emulation. ACS Nano 12(9), 9240–9252 (2018). https://doi.org/10.1021/acsnano.8b03977
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