Issue

Vol. 18 (2026)

Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application

https://doi.org/10.1007/s40820-026-02139-2
Danke Chen
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Peizhi Yu
Department of Precision Instrument, Tsinghua University, Beijing 100049, People’s Republic of China
Yuning Li
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Jingwei Shang
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Haoyuan Wu
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Xuan Yao
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Xiaoqiu Tang
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Chunlong Li
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Mingqiang Zhu
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Chang Gao
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
Jingye Sun
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
He Tian
School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100049, People’s Republic of China
Tao Deng
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, People’s Republic of China

Corresponding Author: Tao Deng

dengtao@bjtu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 310

Abstract

Neuromorphic computing, a highly promising computational architecture, has provided an efficient solution to overcome the limitations of storage–compute separation and scaling constraints. The key to implementing this architecture lies in the development of artificial neurons and synapses as core neuromorphic components capable of biomimicry. Diverse libraries of two-dimensional (2D) materials with atomic-scale thickness and rich tunable physicochemical properties have risen to prominence in recent years. These unique properties meet the critical requirements of neuromorphic devices for ultralow power consumption, dynamic plasticity, and multifunctional integration, thereby facilitating breakthroughs in next-generation high-performance and versatile neuromorphic hardware systems. In this paper, recent advances in dedicated artificial neuron and synapse devices based on 2D materials are reviewed, with a focus on biomimetic models, physical mechanisms, and performance metrics. The discussion further extends to sophisticated switching strategies in reconfigurable components. Then, the systemic integration of neuromorphic devices is summarized, with particular focus on their functional roles in neural perception, neural networks, and logical operation tasks. Finally, a systematic analysis of the limitations at the device and system levels for artificial neurons and synapses is presented, charting a roadmap toward more efficient and multifunctional brain-like chips.


Highlights:
1 This review summarizes recent advances in two-dimensional materials-based artificial neurons and synapses, focusing on their biomimetic models, physical mechanisms, and performance metrics, and further discusses sophisticated switching strategies in reconfigurable components.
2 The systemic integration of neuromorphic devices is presented, with particular emphasis on their functional roles in perception, neural networks, and logical operation tasks.
3 A holistic analysis of the challenge in developing artificial neuronal and synaptic devices and systems is presented, charting a roadmap toward more efficient and multifunctional brain-like chips.

Keywords

Two-dimensional materials Artificial neurons Artificial synapses Brain simulation Reconfigurability
Chen, Danke, Peizhi Yu, Yuning Li, Jingwei Shang, Haoyuan Wu, Xuan Yao, Xiaoqiu Tang, Chunlong Li, Mingqiang Zhu, Chang Gao, Jingye Sun, He Tian, and Tao Deng. 2026. “Dedicated and Reconfigurable Artificial Neurons and Synapses Based on Two-Dimensional Materials for Efficient Neuromorphic Application”. Nano-Micro Letters 18 (March):310. https://doi.org/10.1007/s40820-026-02139-2.
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References
  1. B. von Neumann, Nat. Nanotechnol. 15, 507 (2020). https://doi.org/10.1038/s41565-020-0738-x
  2. A. Wali, S. Das, Two-dimensional memtransistors for non-von Neumann computing: progress and challenges. Adv. Funct. Mater. 34(15), 2308129 (2024). https://doi.org/10.1002/adfm.202308129
  3. X. Zou, S. Xu, X. Chen, L. Yan, Y. Han, Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology. Sci. China Inf. Sci. 64(6), 160404 (2021). https://doi.org/10.1007/s11432-020-3227-1
  4. R. Pollie, Nanosheet chips poised to rescue Moore’s law. Engineering 7(12), 1655–1656 (2021). https://doi.org/10.1016/j.eng.2021.11.008
  5. A. Liu, X. Zhang, Z. Liu, Y. Li, X. Peng et al., The roadmap of 2D materials and devices toward chips. Nano-Micro Lett. 16(1), 119 (2024). https://doi.org/10.1007/s40820-023-01273-5
  6. D. Kudithipudi, C. Schuman, C.M. Vineyard, T. Pandit, C. Merkel et al., Neuromorphic computing at scale. Nature 637(8047), 801–812 (2025). https://doi.org/10.1038/s41586-024-08253-8
  7. P.K. Enuganti, B. Sen Bhattacharya, T. Serrano Gotarredona, O. Rhodes, Neuromorphic computing and applications: a topical review. Wires Data Min. Knowl. Discov. 15(2), e70014 (2025). https://doi.org/10.1002/widm.70014
  8. H. Liu, Y. Qin, H.-Y. Chen, J. Wu, J. Ma et al., Artificial neuronal devices based on emerging materials: neuronal dynamics and applications. Adv. Mater. 35(37), e2205047 (2023). https://doi.org/10.1002/adma.202205047
  9. Y. Wang, Q. Sun, J. Yu, N. Xu, Y. Wei et al., Boolean logic computing based on neuromorphic transistor. Adv. Funct. Mater. 33(47), 2305791 (2023). https://doi.org/10.1002/adfm.202305791
  10. Y. Zhai, P. Xie, J. Hu, X. Chen, Z. Feng et al., Reconfigurable 2D-ferroelectric platform for neuromorphic computing. Appl. Phys. Rev. 10, 011408 (2023). https://doi.org/10.1063/5.0131838
  11. X. Sun, C. Zhu, J. Yi, L. Xiang, C. Ma et al., Reconfigurable logic-in-memory architectures based on a two-dimensional van der Waals heterostructure device. Nat. Electron. 5(11), 752–760 (2022). https://doi.org/10.1038/s41928-022-00858-z
  12. J. Tang, C. He, J. Tang, K. Yue, Q. Zhang et al., A reliable all-2D materials artificial synapse for high energy-efficient neuromorphic computing. Adv. Funct. Mater. 31(27), 2011083 (2021). https://doi.org/10.1002/adfm.202011083
  13. H. Chen, Y. Kang, D. Pu, M. Tian, N. Wan et al., Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors. Nanoscale 15(9), 4309–4316 (2023). https://doi.org/10.1039/d2nr07234c
  14. Z. Zhong, S. Wu, X. Li, Z. Wang, Q. Yang et al., Robust threshold-switching behavior assisted by Cu migration in a ferroionic CuInP2S6 heterostructure. ACS Nano 17(13), 12563–12572 (2023). https://doi.org/10.1021/acsnano.3c02406
  15. J. Huo, H. Yin, Y. Zhang, X. Tan, Y. Mao et al., Quasi-volatile MoS2 barristor memory for 1T compact neuron by correlative charges trapping and Schottky barrier modulation. ACS Appl. Mater. Interfaces 14(51), 57440–57448 (2022). https://doi.org/10.1021/acsami.2c18561
  16. Y. Ji, L. Wang, Y. Long, J. Wang, H. Zheng et al., Ultralow energy adaptive neuromorphic computing using reconfigurable zinc phosphorus trisulfide memristors. Nat. Commun. 16(1), 6899 (2025). https://doi.org/10.1038/s41467-025-62306-8
  17. J. Noh, Y.K. Kim, S. Kang, S.-M. Lee, B.C. Jang et al., Reconfigurable neuron and synapse operations in a steep-switching nonvolatile transistor. Small 21(44), e05649 (2025). https://doi.org/10.1002/smll.202505649
  18. J. Zhou, X. Zhang, Y. Zhang, S. Huang, A. Chen et al., Hardware-implemented DropConnect function for energy-efficient neuromorphic computing. Adv. Funct. Mater. 35(41), 2503452 (2025). https://doi.org/10.1002/adfm.202503452
  19. H. Li, J. Hu, Y. Zhang, A. Chen, L. Lin et al., Boolean computation in single-transistor neuron. Adv. Mater. 36(49), e2409040 (2024). https://doi.org/10.1002/adma.202409040
  20. Y. Yang, X. Zhang, P. Chen, L. Cheng, C. Li et al., Memristive Hodgkin-Huxley neurons with diverse firing patterns for high-order neuromorphic computing. Adv. Intell. Syst. 7(2), 2400383 (2025). https://doi.org/10.1002/aisy.202400383
  21. X.-F. Zhang, J. Ma, Y. Xu, G.-D. Ren, Synchronization between FitzHugh–Nagumo neurons coupled with phototube. Acta Phys. Sin. 70(9), 090502 (2021). https://doi.org/10.7498/aps.70.20201953
  22. X. Fang, S. Duan, L. Wang, Memristive FHN spiking neuron model and brain-inspired threshold logic computing. Neurocomputing 517, 93–105 (2023). https://doi.org/10.1016/j.neucom.2022.08.056
  23. X. Fang, S. Duan, L. Wang, Memristive Izhikevich spiking neuron model and its application in oscillatory associative memory. Front. Neurosci. 16, 885322 (2022). https://doi.org/10.3389/fnins.2022.885322
  24. E.M. Izhikevich, Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004). https://doi.org/10.1109/TNN.2004.832719
  25. L.-F. Xu, C.-D. Li, L. Chen, Contrastive analysis of neuron model. Acta Phys. Sin. 65(24), 240701 (2016). https://doi.org/10.7498/aps.65.240701
  26. L. Yan, Y. Pei, J. Wang, H. He, Y. Zhao et al., High-speed Si films based threshold switching device and its artificial neuron application. Appl. Phys. Lett. 119(15), 153507 (2021). https://doi.org/10.1063/5.0063078
  27. J. Zhao, J. Wang, J. Sun, Y. Shao, Y. Fan et al., Neural morphology perception system based on antiferroelectric AgNbO3 neurons. InfoMat 7(3), e12637 (2025). https://doi.org/10.1002/inf2.12637
  28. J. Li, W. Zhao, C. Fu, Z. Zhai, P. Xu et al., An ion-electronic hybrid artificial neuron with a widely tunable frequency. Nat. Commun. 16(1), 7911 (2025). https://doi.org/10.1038/s41467-025-63195-7
  29. H.E. Plesser, M. Diesmann, Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput. 21(2), 353–359 (2009). https://doi.org/10.1162/neco.2008.03-08-731
  30. L. Chua, Memristor, Hodgkin-Huxley, and edge of chaos. Nanotechnology 24(38), 383001 (2013). https://doi.org/10.1088/0957-4484/24/38/383001
  31. M.D. Pickett, G. Medeiros-Ribeiro, R.S. Williams, A scalable neuristor built with Mott memristors. Nat. Mater. 12(2), 114–117 (2013). https://doi.org/10.1038/nmat3510
  32. L. Qin, P. Guan, J. Shao, Y. Xiao, Y. Yu et al., Molecular crystal memristors. Nat. Nanotechnol. 20(11), 1641–1647 (2025). https://doi.org/10.1038/s41565-025-02013-z
  33. S. Du, W. Yang, H. Gao, W. Dong, B. Xu et al., Sliding memristor in parallel-stacked hexagonal boron nitride. Adv. Mater. 36(35), e2404177 (2024). https://doi.org/10.1002/adma.202404177
  34. K. Ran, J. Jo, S. Cruces, Z. Wang, R.E. Dunin-Borkowski et al., Unraveling the dynamics of conductive filaments in MoS2-based memristors by operando transmission electron microscopy. Nat. Commun. 16(1), 7433 (2025). https://doi.org/10.1038/s41467-025-62592-2
  35. S. Chen, Z. Yang, H. Hartmann, A. Besmehn, Y. Yang et al., Electrochemical ohmic memristors for continual learning. Nat. Commun. 16(1), 2348 (2025). https://doi.org/10.1038/s41467-025-57543-w
  36. X.-D. Li, W. Cheng, L. Liu, Z. Wang, J. Bi, Tuning of the resistive switching mechanism in atomristor based on single-defect. Appl. Phys. Lett. 126(10), 103503 (2025). https://doi.org/10.1063/5.0256120
  37. T. Guo, Z. Pan, Y. Shen, J. Yang, C. Chen et al., Oxygen vacancy induced 2D Bi2SeO5 non-volatile memristor for 1T1R integration. Nano Lett. 25(20), 8258–8266 (2025). https://doi.org/10.1021/acs.nanolett.5c01345
  38. S. Lan, F. Zheng, C. Ding, Y. Hong, B. Wang et al., Advancing high-performance memristors enabled by position-controlled grain boundaries in controllably grown star-shaped MoS2. Nano Lett. 24(48), 15388–15395 (2024). https://doi.org/10.1021/acs.nanolett.4c04642
  39. Y. Li, L. Loh, S. Li, L. Chen, B. Li et al., Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries. Nat. Electron. 4(5), 348–356 (2021). https://doi.org/10.1038/s41928-021-00573-1
  40. N. Kim, K.A. Nirmal, H.J. Lee, T.G. Kim, Miniature OLEDs driven by memristive switching electrodes for compact display configuration. Small 21(43), e06631 (2025). https://doi.org/10.1002/smll.202506631
  41. Y. Zhang, S. Sang, Y. Ge, X. Chai, Y. Liu, Unraveling the defect-induced resistance mechanism of MoS2 memristors at the atomic scale. Appl. Surf. Sci. 715, 164614 (2026). https://doi.org/10.1016/j.apsusc.2025.164614
  42. Y. Qin, M. Wu, N. Yu, Z. Chen, J. Yuan et al., Threshold switching memristor based on 2D SnSe for nociceptive and leaky-integrate and fire neuron simulation. ACS Appl. Electron. Mater. 6(7), 4939–4947 (2024). https://doi.org/10.1021/acsaelm.4c00482
  43. S. Cruces, M.D. Ganeriwala, J. Lee, L. Völkel, D. Braun et al., Volatile MoS2 memristors with lateral silver ion migration for artificial neuron applications. Small Sci. 5(5), 2400523 (2025). https://doi.org/10.1002/smsc.202400523
  44. Y. Gao, H. Nan, R. Qi, C. Wang, S. Xiao et al., Phase-changeable two-dimensional materials: classification, mechanisms, and applications. J. Ind. Eng. Chem. 144, 158–174 (2025). https://doi.org/10.1016/j.jiec.2024.10.003
  45. Z. Wu, F. Niu, D. Chen, Y. Huang, G. Liu et al., Phase transformation of two-dimensional nanomaterials: state-of-the-art progress in designing strategies and catalytic applications. Sci. China Mater. 68(1), 65–85 (2025). https://doi.org/10.1007/s40843-024-3141-4
  46. W. Li, X. Qian, J. Li, Phase transitions in 2D materials. Nat. Rev. Mater. 6(9), 829–846 (2021). https://doi.org/10.1038/s41578-021-00304-0
  47. L. Zhong, W. Xie, J. Yin, W. Jie, Chemical-vapor-deposited 2D VSe2 nanosheet with threshold switching behaviors for Boolean logic calculations and leaky integrate-and-fire functions. J. Mater. Chem. C 11(15), 5032–5038 (2023). https://doi.org/10.1039/d3tc00221g
  48. H. Liu, T. Wu, X. Yan, J. Wu, N. Wang et al., A tantalum disulfide charge-density-wave stochastic artificial neuron for emulating neural statistical properties. Nano Lett. 21(8), 3465–3472 (2021). https://doi.org/10.1021/acs.nanolett.1c00108
  49. H. Choi, S. Baek, H. Jung, T. Kang, S. Lee et al., Spiking neural network integrated with impact ionization field-effect transistor neuron and a ferroelectric field-effect transistor synapse. Adv. Mater. 37(26), 2406970 (2025). https://doi.org/10.1002/adma.202406970
  50. C. Kang, H. Choi, H. Son, T. Kang, S.-M. Lee et al., A steep-switching impact ionization-based threshold switching field-effect transistor. Nanoscale 15(12), 5771–5777 (2023). https://doi.org/10.1039/d2nr06547a
  51. H. Wang, Y. Lu, S. Liu, J. Yu, M. Hu et al., Adaptive neural activation and neuromorphic processing via drain-injection threshold-switching float gate transistor memory. Adv. Mater. 35(52), e2309099 (2023). https://doi.org/10.1002/adma.202309099
  52. 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
  53. Y. Wang, S. Gou, X. Dong, X. Chen, X. Wang et al., A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nat. Electron. 8(8), 680–688 (2025). https://doi.org/10.1038/s41928-025-01433-y
  54. J.-N. Huang, T. Wang, H.-M. Huang, X. Guo, Adaptive SRM neuron based on NbO memristive device for neuromorphic computing. Chip 1(2), 100015 (2022). https://doi.org/10.1016/j.chip.2022.100015
  55. H. Lee, S.W. Cho, S.J. Kim, J. Lee, K.S. Kim et al., Three-terminal ovonic threshold switch (3T-OTS) with tunable threshold voltage for versatile artificial sensory neurons. Nano Lett. 22(2), 733–739 (2022). https://doi.org/10.1021/acs.nanolett.1c04125
  56. S. Saha, V. Adepu, P. Sahatiya, S.S. Dan, Mimicking somatic behavior of neurons using the integrate and fire model of 2D SnS memristive switching characteristics. Appl. Phys. Lett. 125(19), 194101 (2024). https://doi.org/10.1063/5.0226599
  57. Z. Zhou, L. Wang, G. Liu, Y. Li, Z. Guan et al., A facile photonics reconfigurable memristor with dynamically allocated neurons and synapses functions. Light Sci. Appl. 14(1), 269 (2025). https://doi.org/10.1038/s41377-025-01928-5
  58. Y. Qu, M. Hao, H. Hao, S. Ke, Y. Li et al., 2D vanadium carbide/oxide heterostructure-based artificial sensory neuron for multi-color near-infrared object recognition. Adv. Mater. (2025). https://doi.org/10.1002/adma.202512238
  59. J. Wang, C. Teng, Z. Zhang, W. Chen, J. Tan et al., A scalable artificial neuron based on ultrathin two-dimensional titanium oxide. ACS Nano 15(9), 15123–15131 (2021). https://doi.org/10.1021/acsnano.1c05565
  60. S. Hao, X. Ji, S. Zhong, K.Y. Pang, K.G. Lim et al., A monolayer leaky integrate-and-fire neuron for 2D memristive neuromorphic networks. Adv. Electron. Mater. 6(4), 1901335 (2020). https://doi.org/10.1002/aelm.201901335
  61. R. Yu, X. Zhang, C. Gao, E. Li, Y. Yan et al., Low-voltage solution-processed artificial optoelectronic hybrid-integrated neuron based on 2D MXene for multi-task spiking neural network. Nano Energy 99, 107418 (2022). https://doi.org/10.1016/j.nanoen.2022.107418
  62. B. Zeng, X. Zhang, C. Gao, Y. Zou, X. Yu et al., MXene-based memristor for artificial optoelectronic neuron. IEEE Trans. Electron Devices 70(3), 1359–1365 (2023). https://doi.org/10.1109/TED.2023.3234881
  63. Y. Chen, Y. Wang, Y. Luo, X. Liu, Y. Wang et al., Realization of artificial neuron using MXene bi-directional threshold switching memristors. IEEE Electron Device Lett. 40(10), 1686–1689 (2019). https://doi.org/10.1109/LED.2019.2936261
  64. 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
  65. H. Li, J. Hu, A. Chen, C. Wang, L. Chen et al., Single-transistor neuron with excitatory-inhibitory spatiotemporal dynamics applied for neuronal oscillations. Adv. Mater. 34(51), e2207371 (2022). https://doi.org/10.1002/adma.202207371
  66. H. Wang, Y. Xu, R. Yang, X. Miao, A LIF neuron with adaptive firing frequency based on the GaSe memristor. IEEE Trans. Electron Devices 70(8), 4484–4487 (2023). https://doi.org/10.1109/TED.2023.3288508
  67. Z. Zhang, S. Gao, Z. Li, Y. Xu, R. Yang et al., Artificial LIF neuron with bursting behavior based on threshold switching device. IEEE Trans. Electron Devices 70(3), 1374–1379 (2023). https://doi.org/10.1109/TED.2023.3236906
  68. H. Kalita, A. Krishnaprasad, N. Choudhary, S. Das, D. Dev et al., Artificial neuron using vertical MoS2/graphene threshold switching memristors. Sci. Rep. 9(1), 53 (2019). https://doi.org/10.1038/s41598-018-35828-z
  69. S. Baek, Y.K. Kim, S.-M. Lee, H. Choi, J.-S. Park et al., Steep-slope CuInP2S6 ferroionic threshold switching field-effect transistor for implementation of artificial spiking neuron. Adv. Mater. 37(44), e06921 (2025). https://doi.org/10.1002/adma.202506921
  70. Y. Wang, X. Chen, D. Shen, M. Zhang, X. Chen et al., Artificial neurons based on Ag/ V2C /W threshold switching memristors. Nanomaterials 11(11), 2860 (2021). https://doi.org/10.3390/nano11112860
  71. X.-J. Lian, J.-K. Fu, Z.-X. Gao, S.-P. Gu, L. Wang, High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors. Chin. Phys. B 32(1), 017304 (2023). https://doi.org/10.1088/1674-1056/ac673f
  72. R.H. Rupom, M. Jung, A. Pathak, J. Park, E. Lee et al., Ion-induced phase changes in 2D MoTe2 films for neuromorphic synaptic device applications. ACS Nano 19(2), 2529–2539 (2025). https://doi.org/10.1021/acsnano.4c13915
  73. L. Kou, R. Sadri, D. Momodu, E.P.L. Roberts, M.A.S. Mohammad Haniff et al., N-doped graphene/MXene nanocomposite as a temperature-adaptive neuromorphic memristor. ACS Appl. Nano Mater. 7(4), 3631–3644 (2024). https://doi.org/10.1021/acsanm.3c05007
  74. Y. Zhao, Y. Jin, X. Wang, J. Zhao, S. Wu et al., A high linearity and energy-efficient artificial synaptic device based on scalable synthesized MoS2. J. Mater. Chem. C 11(17), 5616–5624 (2023). https://doi.org/10.1039/d3tc00438d
  75. L. Tang, C. Teng, R. Xu, Z. Zhang, U. Khan et al., Controlled growth of wafer-scale transition metal dichalcogenides with a vertical composition gradient for artificial synapses with high linearity. ACS Nano 16(8), 12318–12327 (2022). https://doi.org/10.1021/acsnano.2c03263
  76. T. Wang, J. Meng, X. Zhou, Y. Liu, Z. He et al., Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat. Commun. 13(1), 7432 (2022). https://doi.org/10.1038/s41467-022-35160-1
  77. T. Zeng, S. Shi, K. Hu, L. Jia, B. Li et al., Approaching the ideal linearity in epitaxial crystalline-type memristor by controlling filament growth. Adv. Mater. 36(29), e2401021 (2024). https://doi.org/10.1002/adma.202401021
  78. Y.-C. Shen, C.-Y. Chen, L. Lee, C.-H. Chiang, Y.-C. Hsu et al., Two-dimensional (2D) materials-inserted conductive bridge random access memory: controllable injection of cations in vertical stacking alignment of MoSe2 layers prepared by plasma-assisted chemical vapor reaction. ACS Mater. Lett. 5(5), 1401–1410 (2023). https://doi.org/10.1021/acsmaterialslett.3c00013
  79. Y. Li, S. Ling, R. He, C. Zhang, Y. Dong et al., A robust graphene oxide memristor enabled by organic pyridinium intercalation for artificial biosynapse application. Nano Res. 16(8), 11278–11287 (2023). https://doi.org/10.1007/s12274-023-5789-5
  80. 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
  81. P. Valerius, S. Kretschmer, B.V. Senkovskiy, S. Wu, J. Hall et al., Reversible crystalline-to-amorphous phase transformation in monolayer MoS2 under grazing ion irradiation. 2D Mater. 7(2), 025005 (2020). https://doi.org/10.1088/2053-1583/ab5df4
  82. M. Xiao, Z. Wu, G. Liu, X. Liao, J. Yuan et al., Spatially controlled phase transition in MoTe2 driven by focused ion beam irradiations. ACS Appl. Mater. Interfaces 16(24), 31747–31755 (2024). https://doi.org/10.1021/acsami.4c03546
  83. H. Ryu, Y. Lee, J.H. Jeong, Y. Lee, Y. Cheon et al., Laser-induced phase transition and patterning of hBN-encapsulated MoTe2. Small 19(17), e2205224 (2023). https://doi.org/10.1002/smll.202205224
  84. C. Kim, S. Issarapanacheewin, I. Moon, K.Y. Lee, C. Ra et al., High-electric-field-induced phase transition and electrical breakdown of MoTe2. Adv. Electron. Mater. 6(3), 1900964 (2020). https://doi.org/10.1002/aelm.201900964
  85. S. Song, K.-H. Kim, R. Keneipp, M. Jung, N. Trainor et al., High current and carrier densities in 2D MoS2/AlScN field-effect transistors via ferroelectric gating and ohmic contacts. ACS Nano 19(9), 8985–8996 (2025). https://doi.org/10.1021/acsnano.4c17301
  86. Y. Wang, J. Xiao, H. Zhu, Y. Li, Y. Alsaid et al., Structural phase transition in monolayer MoTe2 driven by electrostatic doping. Nature 550(7677), 487–491 (2017). https://doi.org/10.1038/nature24043
  87. Y. Zhao, Y. Li, M. Liu, K. Xu, F. Ma, Rotation angle dependent Li intercalation and the induced phase transition in bilayer MoTe2. Surf. Interfaces 42, 103497 (2023). https://doi.org/10.1016/j.surfin.2023.103497
  88. A.S. Ahmad, M. Bhullar, K. Stahl, W. Lu, T. Chen et al., Pressure-induced metallization and isostructural transitions in 3R-MoS2. Adv. Sci. 12(35), e05031 (2025). https://doi.org/10.1002/advs.202505031
  89. S. Khadka, L.C. Gallington, B. Freelon, Assessment of thermally driven local structural phase changes in 1T′–MoTe2. Phys. Rev. B 111(21), 214102 (2025). https://doi.org/10.1103/physrevb.111.214102
  90. Z. Yang, D. Zhang, J. Cai, C. Gong, Q. He et al., Joule heating induced non-melting phase transition and multi-level conductance in MoTe2 based phase change memory. Appl. Phys. Lett. 121(20), 203508 (2022). https://doi.org/10.1063/5.0127160
  91. S. Choi, J. Yang, G. Wang, Emerging memristive artificial synapses and neurons for energy-efficient neuromorphic computing. Adv. Mater. 32(51), e2004659 (2020). https://doi.org/10.1002/adma.202004659
  92. S. Pace, L. Martini, D. Convertino, D.H. Keum, S. Forti et al., Synthesis of large-scale monolayer 1T’- MoTe2 and its stabilization via scalable hBN encapsulation. ACS Nano 15(3), 4213–4225 (2021). https://doi.org/10.1021/acsnano.0c05936
  93. X. Pan, Y. Li, B. Cheng, S.-J. Liang, F. Miao, 2D materials for intelligent devices. Sci. China Phys. Mech. Astron. 66(11), 117504 (2023). https://doi.org/10.1007/s11433-022-2056-1
  94. H. Wang, W. Lu, S. Hou, B. Yu, Z. Zhou et al., A 2D-SnSe film with ferroelectricity and its bio-realistic synapse application. Nanoscale 12(42), 21913–21922 (2020). https://doi.org/10.1039/d0nr03724a
  95. Y. Chen, Y. Zhou, F. Zhuge, B. Tian, M. Yan et al., Graphene–ferroelectric transistors as complementary synapses for supervised learning in spiking neural network. npj 2D Mater. Appl. 3, 31 (2019). https://doi.org/10.1038/s41699-019-0114-6
  96. S. Wang, L. Liu, L. Gan, H. Chen, X. Hou et al., Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing. Nat. Commun. 12, 53 (2021). https://doi.org/10.1038/s41467-020-20257-2
  97. C. Yang, Y. Huang, K. Pei, X. Long, L. Yang et al., Current-controllable and reversible multi-resistance-state based on domain wall number transition in 2D ferromagnet Fe3GeTe2. Adv. Mater. 36(18), e2311831 (2024). https://doi.org/10.1002/adma.202311831
  98. P. Huang, X. Liu, Y. Xin, Y. Gu, A. Lee et al., Integrated artificial neural network with trainable activation function enabled by topological insulator-based spin–orbit torque devices. ACS Nano 18(43), 29469–29478 (2024). https://doi.org/10.1021/acsnano.4c03278
  99. C. Hu, L. Liang, J. Yu, L. Cheng, N. Zhang et al., Neuromorphic floating-gate memory based on 2D materials. Cyborg Bionic Syst. 6, 0256 (2025). https://doi.org/10.34133/cbsystems.0256
  100. A. Ghosh, L.U. Vinzons, A. Šlechta, V. Kledrowetz, Y.-F. Lin et al., Versatile dual-gate 2D transistor for logic-in-memory and neuromodulation applications. Small (2025). https://doi.org/10.1002/smll.202503991
  101. Z. Yang, S. Huo, Z. Zhang, F. Meng, B. Liu et al., High-precision multibit opto-electronic synapses based on ReS2/h-BN/graphene heterostructure for energy-efficient and high-accuracy neuromorphic computing. Adv. Funct. Mater. 35(48), 2509119 (2025). https://doi.org/10.1002/adfm.202509119
  102. D.-W. Cao, Y. Yan, M.-N. Wang, G.-L. Luo, J.-R. Zhao et al., Layered wide bandgap semiconductor GaPS4 as a charge-trapping medium for use in high-temperature artificial synaptic applications. Adv. Funct. Mater. 34(28), 2314649 (2024). https://doi.org/10.1002/adfm.202314649
  103. J. Zou, S. Lin, T. Huang, H. Liu, Y. Liu et al., Power efficient MoS2 synaptic devices based on Maxwell–Wagner interfacial charging in binary oxides. 2D Mater. 11(1), 015009 (2024). https://doi.org/10.1088/2053-1583/ad015f
  104. D. Tan, Z.-D. Luo, Q. Yang, F. Xiao, X. Gan et al., Reconfigurable logic and in-memory computing based on electrically controlled charge trapping in dielectric engineered 2D semiconductor transistors. Adv. Funct. Mater. 35(13), 2417887 (2025). https://doi.org/10.1002/adfm.202417887
  105. X. Lin, Y. Li, Y. Lei, Q. Sun, Electric-double-layer-gated 2D transistors for bioinspired sensors and neuromorphic devices. Int. J. Smart Nano Mater. 15(1), 238–259 (2024). https://doi.org/10.1080/19475411.2024.2306837
  106. T. Mei, F. Chen, T. Huang, Z. Feng, T. Wan et al., Ion-electron interactions in 2D nanomaterials-based artificial synapses for neuromorphic applications. ACS Nano 19(18), 17140–17172 (2025). https://doi.org/10.1021/acsnano.5c02397
  107. L. Zhu, C. Dai, S. Chen, A. Liu, R. Zhan et al., Tunable synaptic plasticity in MoS2 neuromorphic transistors using Li+ incorporated chitosan electrolytes. J. Mater. Chem. C 13(18), 9374–9381 (2025). https://doi.org/10.1039/d5tc00877h
  108. Y. Jeong, P. Tordi, A. Tamayo, B. Han, M. Bonini et al., Mimicking synaptic plasticity: optoionic MoS2 Memory powered by biopolymer hydrogels as a dynamic cations reservoir. Adv. Funct. Mater. 35(50), e09607 (2025). https://doi.org/10.1002/adfm.202509607
  109. P. Liu, H. Luo, X. Yin, X. Wang, X. He et al., A memristor based on two-dimensional MoSe2/MoS2 heterojunction for synaptic device application. Appl. Phys. Lett. 121(23), 233501 (2022). https://doi.org/10.1063/5.0127880
  110. Y. Jo, G. Noh, E. Park, D.K. Lee, Y. Jeong et al., Effects of voltage schemes on the conductance modulation of artificial synaptic device based on 2D hBN memristor: its applications for pattern classifications. Chaos Solitons Fractals 187, 115390 (2024). https://doi.org/10.1016/j.chaos.2024.115390
  111. Y. Chen, J. Hua, Y. Li, Q. Zhang, H. Shao et al., Selective release of excitatory-inhibitory neurotransmitters emulated by unipolar synaptic transistors via gate voltage amplitude modulation. Adv. Mater. Technol. 8(5), 2201367 (2023). https://doi.org/10.1002/admt.202201367
  112. Y. Li, M. Zhao, X. Ma, L. Zhang, S. Zhao et al., Reconfigurable, nonvolatile, optoelectronic synaptic memtransistor based on MoS2/Te van der Waals heterostructures. Adv. Funct. Mater. 35(34), 2423333 (2025). https://doi.org/10.1002/adfm.202423333
  113. X. Liu, S. Wang, Z. Di, H. Wu, C. Liu et al., An optoelectronic synapse based on two-dimensional violet phosphorus heterostructure. Adv. Sci. 10(22), 2301851 (2023). https://doi.org/10.1002/advs.202301851
  114. Z. Huang, L. Wang, Y. Zhang, Z. Cheng, F. Chen, Simulation of optoelectronic synaptic behavior in SnSe2/WSe2 heterojunction transistors. ACS Appl. Electron. Mater. 7(5), 1906–1913 (2025). https://doi.org/10.1021/acsaelm.4c02195
  115. 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
  116. T. Zhang, C. Fan, L. Hu, F. Zhuge, X. Pan et al., A reconfigurable all-optical-controlled synaptic device for neuromorphic computing applications. ACS Nano 18(25), 16236–16247 (2024). https://doi.org/10.1021/acsnano.4c02278
  117. C. Han, X. Han, J. Han, M. He, S. Peng et al., Light-stimulated synaptic transistor with high PPF feature for artificial visual perception system application. Adv. Funct. Mater. 32(22), 2113053 (2022). https://doi.org/10.1002/adfm.202113053
  118. D. Xiang, T. Liu, X. Zhang, P. Zhou, W. Chen, Dielectric engineered two-dimensional neuromorphic transistors. Nano Lett. 21(8), 3557–3565 (2021). https://doi.org/10.1021/acs.nanolett.1c00492
  119. Y. Wang, Y. Huang, S. Fan, H. Lu, J. Liu, Heterosynaptic MoSe2 memtransistor array with ultra-low operating voltage and linear plasticity for neuromorphic computing. Chem. Eng. J. 513, 163079 (2025). https://doi.org/10.1016/j.cej.2025.163079
  120. S.J. Kim, I.H. Im, J.H. Baek, S. Choi, S.H. Park et al., Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nat. Nanotechnol. 20(1), 83–92 (2025). https://doi.org/10.1038/s41565-024-01790-3
  121. Z. Dong, Q. Hua, J. Xi, Y. Shi, T. Huang et al., Ultrafast and low-power 2D Bi2O2Se memristors for neuromorphic computing applications. Nano Lett. 23(9), 3842–3850 (2023). https://doi.org/10.1021/acs.nanolett.3c00322
  122. Y. Cao, J. Liang, T. Liu, W. Sang, Y. Gan et al., Reward-modulated spike-timing-dependent plasticity in van der Waals ferroelectric memtransistor for robotic recognition and tracking. Sci. Bull. 70(20), 3351–3360 (2025). https://doi.org/10.1016/j.scib.2025.05.044
  123. D. Cai, Y. Liu, J. Wang, T. Zhao, M. Shen et al., BCM-inspired synapses constructed with barrier-modulated coupling junctions for enhancing speech recognition. Adv. Funct. Mater. 34(27), 2314660 (2024). https://doi.org/10.1002/adfm.202314660
  124. Y. Zhai, P. Xie, Z. Feng, C. Du, S.-T. Han et al., 2D heterostructure for high-order spatiotemporal information processing. Adv. Funct. Mater. 32(7), 2108440 (2022). https://doi.org/10.1002/adfm.202108440
  125. T. Zhao, C. Zhao, W. Xu, Y. Liu, H. Gao et al., Bio-inspired photoelectric artificial synapse based on two-dimensional Ti3C2Tx MXenes floating gate. Adv. Funct. Mater. 31(45), 2106000 (2021). https://doi.org/10.1002/adfm.202106000
  126. Y. Chen, J. Xia, Y. Qu, H. Zhang, T. Mei et al., Ephaptic coupling in ultralow-power ion-gel nanofiber artificial synapses for enhanced working memory. Adv. Mater. 37(16), 2419013 (2025). https://doi.org/10.1002/adma.202419013
  127. Y. Sun, M. Li, Y. Ding, H. Wang, H. Wang et al., Programmable van-der-Waals heterostructure-enabled optoelectronic synaptic floating-gate transistors with ultra-low energy consumption. InfoMat 4(10), e12317 (2022). https://doi.org/10.1002/inf2.12317
  128. Y. Zhang, L. Wang, Z. Huang, W. Deng, X. Yan et al., Nonvolatile memory and neuromorphic devices based on the MoTe2/h-BN/graphene floating gate transistor. Appl. Mater. Today 46, 102908 (2025). https://doi.org/10.1016/j.apmt.2025.102908
  129. Z. Su, H. Cheng, X. Sun, H. Sun, C. Zuo, High-performance floating gate heterostructure with WSe2-MoS2 diode channel for neural synapse. IEEE Electron Device Lett. 44(7), 1084–1087 (2023). https://doi.org/10.1109/LED.2023.3278454
  130. J. Chen, X.-C. Zhao, Y.-Q. Zhu, Z.-H. Wang, Z. Zhang et al., Polarized tunneling transistor for ultralow-energy-consumption artificial synapse toward neuromorphic computing. ACS Nano 18(1), 581–591 (2024). https://doi.org/10.1021/acsnano.3c08632
  131. S. Kanungo, G. Ahmad, P. Sahatiya, A. Mukhopadhyay, S. Chattopadhyay, 2D materials-based nanoscale tunneling field effect transistors: current developments and future prospects. npj 2D Mater. Appl. 6, 83 (2022). https://doi.org/10.1038/s41699-022-00352-2
  132. X. Deng, S.-Q. Wang, Y.-X. Liu, N. Zhong, Y.-H. He et al., A flexible Mott synaptic transistor for nociceptor simulation and neuromorphic computing. Adv. Funct. Mater. 31(23), 2101099 (2021). https://doi.org/10.1002/adfm.202101099
  133. Y. Li, Y. Zhang, Y. Wang, J. Sun, Q. You et al., Polarization-sensitive optoelectronic synapse based on 3D graphene/MoS2 heterostructure. Adv. Funct. Mater. 34(15), 2302288 (2024). https://doi.org/10.1002/adfm.202302288
  134. Y. Chen, Z. Wang, J. Du, C. Si, C. Jiang et al., Wrinkled rhenium disulfide for anisotropic nonvolatile memory and multiple artificial neuromorphic synapses. ACS Nano 18(44), 30871–30883 (2024). https://doi.org/10.1021/acsnano.4c11898
  135. N. Xu, M. Duan, K. Zhang, W. Zhang, C. Jia, Two-dimensional InSe artificial synapses with fJ energy consumption. ACS Appl. Energy Mater. 8(10), 6300–6307 (2025). https://doi.org/10.1021/acsaem.4c03324
  136. Z. Yu, T. Zeng, Z. Han, K. Ye, Y. Zeng et al., Ultralow power artificial synapses based on CrPS4-floating-gate transistor for the emotional simulation. ACS Appl. Nano Mater. 8(3), 1517–1525 (2025). https://doi.org/10.1021/acsanm.4c06195
  137. T.-Y. Wang, J.-L. Meng, Z.-Y. He, L. Chen, H. Zhu et al., Ultralow power wearable heterosynapse with photoelectric synergistic modulation. Adv. Sci. 7(8), 1903480 (2020). https://doi.org/10.1002/advs.201903480
  138. J. Chen, Y.-Q. Zhu, X.-C. Zhao, Z.-H. Wang, K. Zhang et al., PZT-enabled MoS2 floating gate transistors: overcoming Boltzmann tyranny and achieving ultralow energy consumption for high-accuracy neuromorphic computing. Nano Lett. 23(22), 10196–10204 (2023). https://doi.org/10.1021/acs.nanolett.3c02721
  139. M. Huang, W. Ali, L. Yang, J. Huang, C. Yao et al., Multifunctional optoelectronic synapses based on arrayed MoS2 monolayers emulating human association memory. Adv. Sci. 10(16), 2300120 (2023). https://doi.org/10.1002/advs.202300120
  140. X. Li, S. Li, B. Tang, J. Liao, Q. Chen, A vis-SWIR photonic synapse with low power consumption based on WSe2/In2Se3 ferroelectric heterostructure. Adv. Electron. Mater. 8(10), 2200343 (2022). https://doi.org/10.1002/aelm.202200343
  141. Y. Hu, H. Yang, J. Huang, X. Zhang, B. Tan et al., Flexible optical synapses based on In2Se3/MoS2 heterojunctions for artificial vision systems in the near-infrared range. ACS Appl. Mater. Interfaces 14(50), 55839–55849 (2022). https://doi.org/10.1021/acsami.2c19097
  142. C. Zhu, H. Liu, W. Wang, L. Xiang, J. Jiang et al., Optical synaptic devices with ultra-low power consumption for neuromorphic computing. Light. Sci. Appl. 11, 337 (2022). https://doi.org/10.1038/s41377-022-01031-z
  143. M. Ma, C. Huang, M. Yang, D. He, Y. Pei et al., Ultra-low power consumption artificial photoelectric synapses based on Lewis acid doped WSe2 for neuromorphic computing. Small 20(51), 2406402 (2024). https://doi.org/10.1002/smll.202406402
  144. K. Chen, Q. Dai, Y. Tian, W. Chen, C. Ai et al., Ultra-low-power vertical organic synaptic phototransistors for neuromorphic vision preprocessing. Adv. Funct. Mater. 35(48), 2508673 (2025). https://doi.org/10.1002/adfm.202508673
  145. J. Li, X. Wang, Y. Ma, W. Han, K. Li et al., Phase-engineered In2Se3 ferroelectric P-N junctions in phototransistors for ultra-low power and multiscale reservoir computing. ACS Nano 19(13), 13220–13229 (2025). https://doi.org/10.1021/acsnano.5c00250
  146. J. Song, S. Moon, J. Byun, J. Kim, J. Choi et al., Analog switching in hexagonal boron nitride memristors via multiple nano-filaments confinement. Small 21(32), 2504507 (2025). https://doi.org/10.1002/smll.202504507
  147. D. Kumar, H. Li, U.K. Das, A.M. Syed, N. El-Atab, Flexible solution-processable black-phosphorus-based optoelectronic memristive synapses for neuromorphic computing and artificial visual perception applications. Adv. Mater. 35(28), 2370201 (2023). https://doi.org/10.1002/adma.202370201
  148. T. Yu, Z. Zhao, H. Jiang, Z. Weng, Y. Fang et al., MoTe2-based low energy consumption artificial synapse for neuromorphic behavior and decimal arithmetic. Mater. Today Chem. 27, 101268 (2023). https://doi.org/10.1016/j.mtchem.2022.101268
  149. T. Yu, Z. Zhao, H. Jiang, Z. Weng, Y. Fang et al., A low-power memristor based on 2H–MoTe2 nanosheets with synaptic plasticity and arithmetic functions. Mater. Today Nano 19, 100233 (2022). https://doi.org/10.1016/j.mtnano.2022.100233
  150. Z. Yang, Z. Yang, L. Liu, X. Li, J. Li et al., Anisotropic mass transport enables distinct synaptic behaviors on 2D material surface. Mater. Today Electron. 5, 100047 (2023). https://doi.org/10.1016/j.mtelec.2023.100047
  151. 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), e1901423 (2019). https://doi.org/10.1002/smll.201901423
  152. D. Sun, X. Zhu, S. Chen, G. Zhu, G. Lan et al., Uniformity, linearity, and symmetry enhancement in TiOx/MoS2–xOx based analog RRAM via S-vacancy confined nanofilament. Nano Lett. 24(51), 16283–16292 (2024). https://doi.org/10.1021/acs.nanolett.4c04434
  153. W. Hou, A. Azizimanesh, A. Dey, Y. Yang, W. Wang et al., Strain engineering of vertical molybdenum ditelluride phase-change memristors. Nat. Electron. 7(1), 8–16 (2024). https://doi.org/10.1038/s41928-023-01071-2
  154. W.K. Kim, G.Y. Cho, T.T.H. Vu, J.S. Son, Y.H. Shin et al., Robust metal and semiconductor phase transition memristor using Ag-intercalated transition metal dichalcogenide. Adv. Mater. 38(5), e16115 (2026). https://doi.org/10.1002/adma.202516115
  155. L. Qin, Y. Yu, C. Fang, Y. Liu, K. Zhu et al., Intrinsic ion migration-induced susceptible two-dimensional phase-transition memristor with ultralow power consumption. Sci. Bull. 70(13), 2116–2124 (2025). https://doi.org/10.1016/j.scib.2025.03.051
  156. Z. Shen, A. Li, Q. Wang, Y. Cao, Y. Sun et al., Full-vdW heterosynaptic memtransistor with the ferroelectric inserted functional layer and its neuromorphic applications. Adv. Funct. Mater. 35(2), 2412832 (2025). https://doi.org/10.1002/adfm.202412832
  157. C. Zhao, W. Dong, Y. Yang, H. Yu, J. Duan et al., Intrinsic ferroelectric CuVP2S6 for potential applications in neuromorphic recognition and translation. Nat. Commun. 16(1), 6264 (2025). https://doi.org/10.1038/s41467-025-61508-4
  158. M. Wang, D. Ouyang, Y. Dai, D. Huo, W. He et al., 2D piezo-ferro-opto-electronic artificial synapse for bio-inspired multimodal sensory integration. Adv. Mater. 37(24), 2500049 (2025). https://doi.org/10.1002/adma.202500049
  159. D.-W. Cao, Y. Yan, M.-N. Wang, G.-L. Luo, J.-R. Zhao et al., Layered wide bandgap semiconductor GaPS4 as a charge-trapping medium for use in high-temperature artificial synaptic applications. Adv. Funct. Mater. 34(28), 2314649 (2024). https://doi.org/10.1002/adfm.202314649
  160. Y. Chen, G. Gao, J. Zhao, H. Zhang, J. Yu et al., Piezotronic graphene artificial sensory synapse. Adv. Funct. Mater. 29(41), 1900959 (2019). https://doi.org/10.1002/adfm.201900959
  161. W. Deng, Y. Yu, X. Yan, L. Wang, N. Yu et al., Linearly programmable oxygen-doped MoS2 memtransistor for neuromorphic computing. ACS Nano 19(30), 27526–27537 (2025). https://doi.org/10.1021/acsnano.5c06688
  162. S. Lee, R. Peng, C. Wu, M. Li, Programmable black phosphorus image sensor for broadband optoelectronic edge computing. Nat. Commun. 13(1), 1485 (2022). https://doi.org/10.1038/s41467-022-29171-1
  163. X. Zhang, S. Wu, R. Yu, E. Li, D. Liu et al., Programmable neuronal-synaptic transistors based on 2D MXene for a high-efficiency neuromorphic hardware network. Matter 5(9), 3023–3040 (2022). https://doi.org/10.1016/j.matt.2022.06.009
  164. J. Hu, H. Li, Y. Zhang, J. Zhou, Y. Zhao et al., Reconfigurable neuromorphic computing with 2D material heterostructures for versatile neural information processing. Nano Lett. 24(30), 9391–9398 (2024). https://doi.org/10.1021/acs.nanolett.4c02658
  165. C. Yu, S. Li, Z. Pan, Y. Liu, Y. Wang et al., Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor. Nano Lett. 24(5), 1620–1628 (2024). https://doi.org/10.1021/acs.nanolett.3c04193
  166. J. Chen, Z. Wen, F. Yang, R. Bian, Q. Zhang et al., Refreshable memristor via dynamic allocation of ferro-ionic phase for neural reuse. Nat. Commun. 16, 702 (2025). https://doi.org/10.1038/s41467-024-55701-0
  167. Y. Jo, D.Y. Woo, G. Noh, E. Park, M.J. Kim et al., Hardware implementation of network connectivity relationships using 2D hBN-based artificial neuron and synaptic devices. Adv. Funct. Mater. 34(10), 2309058 (2024). https://doi.org/10.1002/adfm.202309058
  168. D.Y. Kang, W.-Y. Lee, N.-W. Park, Y.-S. Yoon, G.-S. Kim et al., Tunable thermal conductivity in aluminum oxide resistive based switching structures by conducting filament diffusion. J. Alloys Compd. 790, 992–1000 (2019). https://doi.org/10.1016/j.jallcom.2019.03.268
  169. S. Deng, H. Yu, T.J. Park, A.N.M.N. Islam, S. Manna et al., Selective area doping for Mott neuromorphic electronics. Sci. Adv. 9(11), eade4838 (2023). https://doi.org/10.1126/sciadv.ade4838
  170. J.C. Li, Z.C. Liu, H.X. Yang, Y.X. Ma, Y.L. Wang, Dual-modes HfLaOx-based memristor with InSe passivation layer. Appl. Surf. Sci. 682, 161630 (2025). https://doi.org/10.1016/j.apsusc.2024.161630
  171. H. Zhou, V. Sorkin, S. Chen, Z. Yu, K.-W. Ang et al., Design-dependent switching mechanisms of Schottky-barrier-modulated memristors based on 2D semiconductor. Adv. Electron. Mater. 9(6), 2201252 (2023). https://doi.org/10.1002/aelm.202201252
  172. R. Peng, Y. Wu, B. Wang, R. Shi, L. Xu et al., Programmable graded doping for reconfigurable Molybdenum ditelluride devices. Nat. Electron. 6(11), 852–861 (2023). https://doi.org/10.1038/s41928-023-01056-1
  173. D. Chen, Y. Li, X. Tang, J. Sun, X. Yao et al., Novel optoelectronic reconfigurable transistors based on graphene/VO2 heterojunction for efficient neuromorphic perception, computation, and storage. Adv. Sci. 12(46), e13429 (2025). https://doi.org/10.1002/advs.202513429
  174. S. Ha, Y. Park, C. Lim, E. Yang, T. Kim et al., Bifunctional memristive behavior of a dual-layer structure depending on the configuration of charge transport. Adv. Electron. Mater. 11(12), 2500029 (2025). https://doi.org/10.1002/aelm.202500029
  175. J. Chen, M. Xiao, Z. Chen, S. Khan, S. Ghosh et al., Inkjet-printed reconfigurable and recyclable memristors on paper. InfoMat 7(5), e70000 (2025). https://doi.org/10.1002/inf2.70000
  176. X. Li, W. Yan, D. Wang, W. Huang, Y. Guo et al., Atomistic mechanisms of the crystallographic orientation-dependent Cu1.8S conductive channel formation in Cu2S-based memristors. Adv. Mater. 37(32), 2501300 (2025). https://doi.org/10.1002/adma.202501300
  177. F. Yang, Y. Xiong, Z. Chen, S. Wang, Y. Wang et al., Reconfigurable high-performance memristors based on few-layer high-κ dielectric Bi2SeO5 for neuromorphic computing. Adv. Funct. Mater. 36(3), e14338 (2026). https://doi.org/10.1002/adfm.202514338
  178. P. Bousoulas, S. Orfanoudakis, D. Spathi, V. Pagonis, L. Tsetseris et al., Low power FA2PbI4/SiO2 bilayer memristors with Pt nanops exhibiting reconfigurable synaptic and neuron properties for compact optoelectronic neuromorphic systems. Nano Lett. 25(41), 14903–14912 (2025). https://doi.org/10.1021/acs.nanolett.5c03475
  179. B. Alqahtani, H. Li, A.M. Syed, N. El-Atab, From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing. Light Sci. Appl. 14(1), 30 (2025). https://doi.org/10.1038/s41377-024-01698-6
  180. J. Huo, L. Li, H. Zheng, J. Gao, T.T. Te Tun et al., Compact physical implementation of spiking neural network using ambipolar WSe2 n-type/p-type ferroelectric field-effect transistor. ACS Nano 18(41), 28394–28405 (2024). https://doi.org/10.1021/acsnano.4c11081
  181. J. Gao, Y.-C. Chien, J. Huo, L. Li, H. Zheng et al., Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics. Nat. Commun. 16(1), 4368 (2025). https://doi.org/10.1038/s41467-025-59603-7
  182. U.Y. Won, Q. An Vu, S.B. Park, M.H. Park, V.D. Do et al., Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning. Nat. Commun. 14(1), 3070 (2023). https://doi.org/10.1038/s41467-023-38667-3
  183. A.-L. Liu, Y.-F. Liu, G. Wang, Y.-Q. Shao, C.-X. Yu et al., The role of ipRGCs in ocular growth and myopia development. Sci. Adv. 8(23), eabm9027 (2022). https://doi.org/10.1126/sciadv.abm9027
  184. S. Zhong, L. Su, M. Xu, D. Loke, B. Yu et al., Recent advances in artificial sensory neurons: biological fundamentals, devices, applications, and challenges. Nano-Micro Lett. 17(1), 61 (2024). https://doi.org/10.1007/s40820-024-01550-x
  185. S.J. Hong, Y.R. Lee, A. Bag, H.S. Kim, T.Q. Trung et al., Bio-inspired artificial mechanoreceptors with built-in synaptic functions for intelligent tactile skin. Nat. Mater. 24(7), 1100–1108 (2025). https://doi.org/10.1038/s41563-025-02204-y
  186. K. Li, J. Yao, P. Zhao, Y. Luo, X. Ge et al., Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing. Mater. Horiz. 11(9), 2106–2114 (2024). https://doi.org/10.1039/d4mh00053f
  187. A.M. Syed, D.D. Kumbhar, H. Li, M.K. Rajbhar, D. Kumar et al., A multimodal humidity adaptive optical neuron based on a MoWS2/VOx heterojunction for vision and respiratory functions. Adv. Mater. 37(27), 2417793 (2025). https://doi.org/10.1002/adma.202417793
  188. M.U.K. Sadaf, N.U. Sakib, A. Pannone, H. Ravichandran, S. Das, A bio-inspired visuotactile neuron for multisensory integration. Nat. Commun. 14(1), 5729 (2023). https://doi.org/10.1038/s41467-023-40686-z
  189. S. Ghosh, A. Pannone, D. Sen, A. Wali, H. Ravichandran et al., An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior. Nat. Commun. 14(1), 6021 (2023). https://doi.org/10.1038/s41467-023-41046-7
  190. H. Ma, H. Fang, X. Xie, Y. Liu, H. Tian et al., Optoelectronic synapses based on MXene/violet phosphorus van der Waals heterojunctions for visual-olfactory crossmodal perception. Nano-Micro Lett. 16(1), 104 (2024). https://doi.org/10.1007/s40820-024-01330-7
  191. Y. Liu, D. Liu, C. Gao, X. Zhang, R. Yu et al., Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing. Nat. Commun. 13(1), 7917 (2022). https://doi.org/10.1038/s41467-022-35628-0
  192. Y. Pei, Z. Li, B. Li, Y. Zhao, H. He et al., A multifunctional and efficient artificial visual perception nervous system with Sb2Se3/CdS-core/shell (SC) nanorod arrays optoelectronic memristor. Adv. Funct. Mater. 32(29), 2203454 (2022). https://doi.org/10.1002/adfm.202203454
  193. S. Ahmed, J. Yi, Two-dimensional transition metal dichalcogenides and their charge carrier mobilities in field-effect transistors. Nano-Micro Lett. 9(4), 50 (2017). https://doi.org/10.1007/s40820-017-0152-6
  194. L. Cheng, C. Zhang, Y. Liu, Intrinsic charge carrier mobility of 2D semiconductors. Comput. Mater. Sci. 194, 110468 (2021). https://doi.org/10.1016/j.commatsci.2021.110468
  195. C. Zhang, R. Wang, H. Mishra, Y. Liu, Two-dimensional semiconductors with high intrinsic carrier mobility at room temperature. Phys. Rev. Lett. 130(8), 087001 (2023). https://doi.org/10.1103/PhysRevLett.130.087001
  196. P.-Y. Huang, B.-Y. Jiang, H.-J. Chen, J.-Y. Xu, K. Wang et al., Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction. Nat. Commun. 14(1), 6736 (2023). https://doi.org/10.1038/s41467-023-42488-9
  197. Y. Hwang, B. Park, S. Hwang, S.-W. Choi, H.S. Kim et al., A bioinspired ultra flexible artificial van der Waals 2D-MoS2 channel/LiSiOx solid electrolyte synapse arrays via laser-lift off process for wearable adaptive neuromorphic computing. Small Methods 7(7), e2201719 (2023). https://doi.org/10.1002/smtd.202201719
  198. Z. Wang, J. Zhu, D. Liu, Q. Ren, R. Qiu et al., Array-level MXene electrode flexible long-term-plasticity synaptic transistors. Mater. Horiz. 12(17), 6842–6852 (2025). https://doi.org/10.1039/d5mh00447k
  199. M. Li, H. Chu, C. Gao, F.-S. Yang, M. Huang et al., Bioinspired high-order in-sensor spatiotemporal enhancement in van der Waals optoelectronic neuromorphic electronics. Nat. Commun. 16, 8801 (2025). https://doi.org/10.1038/s41467-025-63873-6
  200. Y. Ren, Y. Wang, Z.-Y. Xiong, Y. Bai, H. Wen et al., Reconfigurable artificial perception system and logic gates based on large-scale antiambipolar 2D heterostructure array. Adv. Funct. Mater. 35(22), 2417358 (2025). https://doi.org/10.1002/adfm.202417358
  201. A. Krishnaprasad, D. Dev, S.S. Han, Y. Shen, H.-S. Chung et al., MoS2 synapses with ultra-low variability and their implementation in Boolean logic. ACS Nano 16(2), 2866–2876 (2022). https://doi.org/10.1021/acsnano.1c09904
  202. Q. Yang, Z.-D. Luo, D. Zhang, M. Zhang, X. Gan et al., Controlled optoelectronic response in van der Waals heterostructures for in-sensor computing. Adv. Funct. Mater. 32(45), 202207290 (2022). https://doi.org/10.1002/adfm.202207290
  203. J. Gong, Y. Wei, Y. Wang, Z. Feng, J. Yu et al., Brain-inspired multimodal synaptic memory via mechano-photonic plasticized asymmetric ferroelectric heterostructure. Adv. Funct. Mater. 34(48), 2408435 (2024). https://doi.org/10.1002/adfm.202408435
  204. X. Lin, Z. Feng, Y. Xiong, W. Sun, W. Yao et al., Piezotronic neuromorphic devices: principle, manufacture, and applications. Int. J. Extrem. Manuf. 6(3), 032011 (2024). https://doi.org/10.1088/2631-7990/ad339b
  205. J. Zhou, H. Li, M. Tian, A. Chen, L. Chen et al., Multi-stimuli-responsive synapse based on vertical van der Waals heterostructures. ACS Appl. Mater. Interfaces 14(31), 35917–35926 (2022). https://doi.org/10.1021/acsami.2c08335
  206. L. Sun, Y. Xu, G. Huo, Y. Hou, W. Li et al., Multifunctional neuromorphic optoelectronic computing using all 2D floating-gate transistors. Nano Energy 143, 111311 (2025). https://doi.org/10.1016/j.nanoen.2025.111311
  207. B. Pei, X. Han, Y. Wang, J. Liu, Dual-functional optoelectronic synaptic device based on MoTe2/h-BN transistor through UV light induced doping. Small 21(23), e2500184 (2025). https://doi.org/10.1002/smll.202500184
  208. T. Zhang, X. Guo, P. Wang, X. Fan, Z. Wang et al., High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network. Nat. Commun. 15, 2471 (2024). https://doi.org/10.1038/s41467-024-46867-8
  209. Q. He, H. Wang, Y. Zhang, A. Chen, Y. Fu et al., Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing. Nat. Commun. 16(1), 4340 (2025). https://doi.org/10.1038/s41467-025-59815-x
  210. X. Huang, L. Tong, L. Xu, W. Shi, Z. Peng et al., 2D MoS2-based reconfigurable analog hardware. Nat. Commun. 16, 101 (2025). https://doi.org/10.1038/s41467-024-55395-4
  211. Q.A. Vu, H. Kim, V.L. Nguyen, U.Y. Won, S. Adhikari et al., A high-on/off-ratio floating-gate memristor array on a flexible substrate via CVD-grown large-area 2D layer stacking. Adv. Mater. 29(44), 1703363 (2017). https://doi.org/10.1002/adma.201703363
  212. S. Chen, M.R. Mahmoodi, Y. Shi, C. Mahata, B. Yuan et al., Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks. Nat. Electron. 3(10), 638–645 (2020). https://doi.org/10.1038/s41928-020-00473-w
  213. J. Yu, G. Gao, J. Huang, X. Yang, J. Han et al., Contact-electrification-activated artificial afferents at femtojoule energy. Nat. Commun. 12(1), 1581 (2021). https://doi.org/10.1038/s41467-021-21890-1
  214. C. Tan, X. Cao, X.-J. Wu, Q. He, J. Yang et al., Recent advances in ultrathin two-dimensional nanomaterials. Chem. Rev. 117(9), 6225–6331 (2017). https://doi.org/10.1021/acs.chemrev.6b00558
  215. M.A. Islam, P. Serles, B. Kumral, P.G. Demingos, T. Qureshi et al., Exfoliation mechanisms of 2D materials and their applications. Appl. Phys. Rev. 9(4), 041301 (2022). https://doi.org/10.1063/5.0090717
  216. L. Wang, G. Sun, S. Yuan, Chemical vapor deposition growth of 2D ferroelectric materials for device applications. Adv. Mater. Technol. 9(9), 2301973 (2024). https://doi.org/10.1002/admt.202301973
  217. H. Wang, J. Zhang, Y. Tian, L. Gu, Y. Peng et al., Insights into space-confined CVD growth of large-area monolayer h-BN thin films. J. Alloys Compd. 1040, 183287 (2025). https://doi.org/10.1016/j.jallcom.2025.183287
  218. L. Jia, J. Wang, D. Kong, H. Lan, P. Wang et al., Synthesis of large-area 2D transition metal dichalcogenides via chemical vapor deposition. Adv. Funct. Mater. 35(43), 2505971 (2025). https://doi.org/10.1002/adfm.202505971
  219. P. Wang, D. Yang, X. Pi, Toward wafer-scale production of 2D transition metal chalcogenides. Adv. Electron. Mater. 7(8), 2100278 (2021). https://doi.org/10.1002/aelm.202100278
  220. H. Jiang, X. Zhang, K. Chen, X. He, Y. Liu et al., Two-dimensional Czochralski growth of single-crystal MoS2. Nat. Mater. 24(2), 188–196 (2025). https://doi.org/10.1038/s41563-024-02069-7
  221. B. Qin, J. Jiang, L. Wang, Q. Guo, C. Zhang et al., Two-dimensional indium selenide wafers for integrated electronics. Science 389(6757), 299–302 (2025). https://doi.org/10.1126/science.adu3803
  222. X. Zheng, L. Han, S. Fatima, S. Khan, Y. Sun et al., Large-scale and ultraclean dry transfer of two-dimensional materials via liquid nitrogen-assisted cryogenic exfoliation. Nano Lett. 25(25), 9967–9975 (2025). https://doi.org/10.1021/acs.nanolett.5c01548
  223. J. Pyo, J. Lim, J. Byeon, S. Park, S. Kang et al., Etchant-free wafer-scale 2D transfer and van der Waals 3D integration via peel-off force engineering. ACS Nano 19(28), 25860–25869 (2025). https://doi.org/10.1021/acsnano.5c04785
  224. W. Dong, Z. Dai, L. Liu, Z. Zhang, Toward clean 2D materials and devices: recent progress in transfer and cleaning methods. Adv. Mater. 36(22), e2303014 (2024). https://doi.org/10.1002/adma.202303014
  225. L. Liu, P. Gong, K. Liu, A. Nie, Z. Liu et al., Scalable van der Waals encapsulation by inorganic molecular crystals. Adv. Mater. 34(7), e2106041 (2022). https://doi.org/10.1002/adma.202106041
  226. Z.-S. Liao, K.-T. Chen, L.-C. Shih, S.-M. Chen, K.-S. Hsu et al., Rivalling dynamic adaptation of intrinsic neuronal plasticity in dual-mode Mott memristor electronic neuron for spiking control. Adv. Funct. Mater. 35(51), e08585 (2025). https://doi.org/10.1002/adfm.202508585
  227. G. Xue, B. Qin, C. Ma, P. Yin, C. Liu et al., Large-area epitaxial growth of transition metal dichalcogenides. Chem. Rev. 124(17), 9785–9865 (2024). https://doi.org/10.1021/acs.chemrev.3c00851
  228. M. Pradhan, M. Alomari, M. Moser, D. Fahle, H. Hahn et al., Physical modeling of charge trapping effects in GaN/Si devices and incorporation in the ASM-HEMT model. IEEE J. Electron Devices Soc. 9, 748–755 (2021). https://doi.org/10.1109/JEDS.2021.3103596
  229. S. Ma, T. Wu, X. Chen, Y. Wang, J. Ma et al., A 619-pixel machine vision enhancement chip based on two-dimensional semiconductors. Sci. Adv. 8(31), eabn9328 (2022). https://doi.org/10.1126/sciadv.abn9328
  230. Y. Wang, N. Xu, D. Li, J. Zhu, Thermal properties of two dimensional layered materials. Adv. Funct. Mater. 27(19), 1604134 (2017). https://doi.org/10.1002/adfm.201604134
  231. B. Kundu, S. Goswami, Molecular mechanism enabling linearity and symmetry in neuromorphic elements. Appl. Phys. Rev. 12(3), 031410 (2025). https://doi.org/10.1063/5.0256247
  232. G. Shen, C. Zhuge, J. Jiang, Y. Fu, Y. Zheng et al., Defective engineering tuning the analog switching linearity and symmetry of two-terminal artificial synapse for neuromorphic systems. Adv. Funct. Mater. 34, 2309054 (2024). https://doi.org/10.1002/adfm.202309054
  233. H. Greatorex, O. Richter, M. Mastella, M. Cotteret, P. Klein et al., A neuromorphic processor with on-chip learning for beyond-CMOS device integration. Nat. Commun. 16, 6424 (2025). https://doi.org/10.1038/s41467-025-61576-6
  234. B. Cramer, D. Stöckel, M. Kreft, M. Wibral, J. Schemmel et al., Control of criticality and computation in spiking neuromorphic networks with plasticity. Nat. Commun. 11(1), 2853 (2020). https://doi.org/10.1038/s41467-020-16548-3
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