Issue

Vol. 17 (2025)

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

https://doi.org/10.1007/s40820-025-01705-4
Zhipeng Xia
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China
Xiao Sun
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China
Zhenlong Wang
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China
Jialin Meng
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China
Boyan Jin
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China
Tianyu Wang
School of Integrated Circuits, Shandong University, Jinan 250100, People’s Republic of China

Corresponding Author: Tianyu Wang

tywang@sdu.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 217

Abstract

As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.


Highlights:
1 This review describes various types of low-power memristors, demonstrating their potential for a wide range of applications.
2 This review summarizes low-power memristors for multi-level storage, digital logic, and neuromorphic computing, emphasizing their use as artificial synapses and neurons in artificial neural network, convolutional neural network, and spiking neural network, along with 1T1R and 1S1R crossbar array designs.
3 Further exploration is essential to overcome limitations and unlock the full potential of low-power memristors for in-memory computing and AI.

Keywords

Memristor Low power Multi-value storage Digital logic gates Neuromorphic computing
Xia, Zhipeng, Xiao Sun, Zhenlong Wang, Jialin Meng, Boyan Jin, and Tianyu Wang. 2025. “Low-Power Memristor for Neuromorphic Computing: From Materials to Applications”. Nano-Micro Letters 17 (April):217. https://doi.org/10.1007/s40820-025-01705-4.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. M.D. Godfrey, D.F. Hendry, The computer as von Neumann planned it. IEEE Ann. Hist. Comput. 15(1), 11–21 (2002). https://doi.org/10.1109/85.194088
  2. M. Lu, C.N. Christensen, J.M. Weber, T. Konno, N.F. Läubli et al., ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology. Nat. Methods 20(4), 569–579 (2023). https://doi.org/10.1038/s41592-023-01815-0
  3. S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz et al., Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv. arXiv (USA). (2023). https://doi.org/10.48550/arXiv.2303.12712
  4. L. Feng, Application analysis of artificial intelligence algorithms in image processing. . Probl. Eng. 2022, 7382938 (2022). https://doi.org/10.1155/2022/7382938
  5. R.R. Schaller, Moore’s law: past, present and future. IEEE Spectr. 34(6), 52–59 (1997). https://doi.org/10.1109/6.591665
  6. M. Lundstrom, Moore’s law forever? Science 299(5604), 210–211 (2003). https://doi.org/10.1126/science.1079567
  7. X. Ren, D. Lustig, E. Bolotin, A. Jaleel, O. Villa et al., HMG: extending cache coherence protocols across modern hierarchical multi-GPU systems. 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). February 22-26, 2020, San Diego, CA, USA. IEEE, (2020)., pp. 582–595.
  8. R. Buehrer, K. Ekanadham, Incorporating data flow ideas into von Neumann processors for parallel execution. IEEE Trans. Comput. C3-6(12), 1515–1522 (1987). https://doi.org/10.1109/TC.1987.5009501
  9. W. Kautz, Cellular logic-in-memory arrays. IEEE Trans. Comput. C–18, 719–727 (2006). https://doi.org/10.1109/T-C.1969.222754
  10. Y. Zhang, L. Chu, W. Li, A fully-integrated memristor chip for edge learning. Nano-Micro Lett. 16(1), 166 (2024). https://doi.org/10.1007/s40820-024-01368-7
  11. L. Chua, Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971). https://doi.org/10.1109/TCT.1971.1083337
  12. H. H. Li, X. L. Xiong, F. Hui, D. L. Yang et al., Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device. Nanotechnology. 33(46), (2022). https://doi.org/10.1088/1361-6528/ac5f96
  13. H. Zhou, S. Li, K.-W. Ang, Y.-W. Zhang, Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett. 16(1), 121 (2024). https://doi.org/10.1007/s40820-024-01335-2
  14. Q. Y. Zhang, Z. R. Zhang, C. Li, R. J. Xu et al., Van der Waals materials-based floating gate memory for neuromorphic computing. Chip. 2(4), (2023). https://doi.org/10.1016/j.chip.2023.100059
  15. E. Kim, G. Hwang, D. Kim et al., Orbital Gating Driven by Giant Stark Effect in Tunneling Phototransistors. Advanced Materials. 34(6), (2022). https://doi.org/10.1002/adma.202106625
  16. J. Qiu, J. Li, W. Li, K. Wang, S. Zhang et al., Advancements in nanowire-based devices for neuromorphic computing: a review. ACS Nano 18(46), 31632–31659 (2024). https://doi.org/10.1021/acsnano.4c10170
  17. J. Wang, L. Wang, J. Liu, Overview of phase-change materials based photonic devices. IEEE Access 8, 121211–121245 (2020). https://doi.org/10.1109/ACCESS.2020.3006899
  18. A.I. Khan, H. Kwon, M.E. Chen, M. Asheghi, H.S. PhilipWong et al., Electro-thermal confinement enables improved superlattice phase change memory. IEEE Electron Device Lett. 43(2), 204–207 (2022). https://doi.org/10.1109/LED.2021.3133906
  19. Z. Yang, B. Li, J.-J. Wang, X.-D. Wang, M. Xu et al., Designing conductive-bridge phase-change memory to enable ultralow programming power. Adv. Sci. 9(8), e2103478 (2022). https://doi.org/10.1002/advs.202103478
  20. R. Ramesh, S. Salahuddin, S. Datta, C.H. Diaz, D.E. Nikonov et al., Roadmap on low-power electronics. APL Mater. 12(9), 099201 (2024). https://doi.org/10.1063/5.0184774
  21. Y. Liu, C. Ye, K.-C. Chang, L. Li, B. Jiang et al., A robust and low-power bismuth doped tin oxide memristor derived from coaxial conductive filaments. Small 16(46), e2004619 (2020). https://doi.org/10.1002/smll.202004619
  22. X. Guo, Q. Wang, X. Lv, H. Yang, K. Sun et al., SiO2/Ta2O5 heterojunction ECM memristors: physical nature of their low voltage operation with high stability and uniformity. Nanoscale 12(7), 4320–4327 (2020). https://doi.org/10.1039/c9nr09845c
  23. Y. Zhang, X. Jia, J. Xu, Z. Guo, W. Zhang et al., Near-sensor analog computing system based on low-power and self-assembly nanoscaffolded BaTiO3: Nd2O3 memristor. Nano Today 55, 102144 (2024). https://doi.org/10.1016/j.nantod.2023.102144
  24. S. Afshari, S. Radhakrishnan, J. Xie, M. Musisi-Nkambwe, J. Meng et al., Dot-product computation and logistic regression with 2D hexagonal-boron nitride (h-BN) memristor arrays. 2D Mater. 10(3), 035031 (2023). https://doi.org/10.1088/2053-1583/acdfe1
  25. S.S. TejaNibhanupudi, A. Roy, D. Veksler, M. Coupin, K.C. Matthews et al., Ultra-fast switching memristors based on two-dimensional materials. Nat. Commun. 15(1), 2334 (2024). https://doi.org/10.1038/s41467-024-46372-y
  26. D. Lee, S.-M. Kim, J.-C. Park, Y. Jung, S. Lee et al., Enhancing reliability in oxide-based memristors using two-dimensional transition metal dichalcogenides. Appl. Surf. Sci. 679, 161216 (2025). https://doi.org/10.1016/j.apsusc.2024.161216
  27. Z.-J. Chen, Z. Tang, Z.-Y. Fan, J.-L. Fang, F. Qiu et al., A flexible artificial synapse based on the two-dimensional CuInS2 memristor for neural morphology calculation. Mater. Sci. Semicond. Process. 188, 109203 (2025). https://doi.org/10.1016/j.mssp.2024.109203
  28. B. Ku, B. Koo, A.S. Sokolov, M.J. Ko, C. Choi, Two-terminal artificial synapse with hybrid organic-inorganic perovskite (CH3NH3)PbI3 and low operating power energy (∼47 fJ/μm2). J. Alloys Compounds 833, 155064 (2020). https://doi.org/10.1016/j.jallcom.2020.155064
  29. S.J. Kim, I.H. Im, J.H. Baek, S.H. Park, J.Y. Kim et al., Reliable and robust two-dimensional perovskite memristors for flexible-resistive random-access memory array. ACS Nano 18(41), 28131–28141 (2024). https://doi.org/10.1021/acsnano.4c07673
  30. C. Wu, T.W. Kim, H.Y. Choi, D.B. Strukov, J.J. Yang, Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability. Nat. Commun. 8(1), 752 (2017). https://doi.org/10.1038/s41467-017-00803-1
  31. X. Yang, J. Huang, S. Gao, Y. Zhao, T. Huang et al., Solution-processed hydrogen-bonded organic framework nanofilms for high-performance resistive memory devices. Adv. Mater. 35(47), e2305344 (2023). https://doi.org/10.1002/adma.202305344
  32. Y. Wei, Y. Liu, Q. Lin, T. Liu, S. Wang et al., Organic optoelectronic synapses for sound perception. Nano-Micro Lett. 15(1), 133 (2023). https://doi.org/10.1007/s40820-023-01116-3
  33. S. Kim, S. Choi, W. Lu, Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano 8(3), 2369–2376 (2014). https://doi.org/10.1021/nn405827t
  34. 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
  35. X. Wang, S. Song, H. Wang, T. Guo, Y. Xue et al., Minimizing the programming power of phase change memory by using graphene nanoribbon edge-contact. Adv. Sci. 9(25), e2202222 (2022). https://doi.org/10.1002/advs.202202222
  36. K. Ding, B. Chen, Y. Chen, J. Wang, X. Shen et al., Recipe for ultrafast and persistent phase-change memory materials. NPG Asia Mater. 12, 63 (2020). https://doi.org/10.1038/s41427-020-00246-z
  37. Z.-C. Pan, D. Li, X.-G. Ye, Z. Chen, Z.-H. Chen et al., Room-temperature orbit-transfer torque enabling van der Waals magnetoresistive memories. Sci. Bull. 68(22), 2743–2749 (2023). https://doi.org/10.1016/j.scib.2023.10.008
  38. V.J. Borràs, R. Carpenter, L. Žaper, S. Rao, S. Couet et al., A quantum sensing metrology for magnetic memories. NPJ Spintron. 2(1), 14 (2024). https://doi.org/10.1038/s44306-024-00016-5
  39. C. Ma, Z. Luo, W. Huang, L. Zhao, Q. Chen et al., Sub-nanosecond memristor based on ferroelectric tunnel junction. Nat. Commun. 11(1), 1439 (2020). https://doi.org/10.1038/s41467-020-15249-1
  40. H. Wang, Z. Guan, J. Li, Z. Luo, X. Du et al., Silicon-compatible ferroelectric tunnel junctions with a SiO2/Hf0.5Zr0.5O2 composite barrier as low-voltage and ultra-high-speed memristors. Adv. Mater. 36(15), 2211305 (2024). https://doi.org/10.1002/adma.202211305
  41. N. Yang, J. Zhang, J.-K. Huang, Y. Liu, J. Shi et al., Multitasking memristor for high performance and ultralow power artificial synaptic device application. ACS Appl. Electron. Mater. 4(6), 3154–3165 (2022). https://doi.org/10.1021/acsaelm.2c00663
  42. M.C. Sahu, A.K. Jena, S.K. Mallik, S. Roy, S. Sahoo et al., Reconfigurable low-power TiO2 memristor for integration of artificial synapse and nociceptor. ACS Appl. Mater. Interfaces 15(21), 25713–25725 (2023). https://doi.org/10.1021/acsami.3c02727
  43. 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
  44. T.Y. Wang, J.L. Meng, M.Y. Rao, Z.Y. He, L. Chen et al., Three-dimensional nanoscale flexible memristor networks with ultralow power for information transmission and processing application. Nano Lett. 20(6), 4111–4120 (2020). https://doi.org/10.1021/acs.nanolett.9b05271
  45. X. Feng, Y. Li, L. Wang, S. Chen, Z.G. Yu et al., A fully printed flexible MoS2 memristive artificial synapse with femtojoule switching energy. Adv. Electron. Mater. 5(12), 1900740 (2019). https://doi.org/10.1002/aelm.201900740
  46. J.-M. Yang, Y.-K. Jung, J.-H. Lee, Y.C. Kim, S.-Y. Kim et al., Asymmetric carrier transport in flexible interface-type memristor enables artificial synapses with sub-femtojoule energy consumption. Nanoscale Horiz. 6(12), 987–997 (2021). https://doi.org/10.1039/D1NH00452B
  47. L. Sun, Y. Zhang, G. Hwang, J. Jiang, D. Kim et al., Synaptic computation enabled by joule heating of single-layered semiconductors for sound localization. Nano Lett. 18(5), 3229–3234 (2018). https://doi.org/10.1021/acs.nanolett.8b00994
  48. K. Xu, T. Wang, C. Lu, Y. Song, Y. Liu et al., Novel two-terminal synapse/neuron based on an antiferroelectric hafnium zirconium oxide device for neuromorphic computing. Nano Lett. 24(36), 11170–11178 (2024). https://doi.org/10.1021/acs.nanolett.4c02142
  49. C. Yoon, J.H. Lee, S. Lee, J.H. Jeon, J.T. Jang et al., Synaptic plasticity selectively activated by polarization-dependent energy-efficient ion migration in an ultrathin ferroelectric tunnel junction. Nano Lett. 17(3), 1949–1955 (2017). https://doi.org/10.1021/acs.nanolett.6b05308
  50. P. Gao, M. Duan, G. Yang, W. Zhang, C. Jia, Ultralow energy consumption and fast neuromorphic computing based on La0.1Bi0.9FeO3 ferroelectric tunnel junctions. Nano Lett. 24(35), 10767–10775 (2024). https://doi.org/10.1021/acs.nanolett.4c01924
  51. T. Tuma, A. Pantazi, M. Le Gallo, A. Sebastian, E. Eleftheriou, Stochastic phase-change neurons. Nat. Nanotechnol. 11(8), 693–699 (2016). https://doi.org/10.1038/nnano.2016.70
  52. D. Kuzum, R.G. Jeyasingh, B. Lee, H.S. Wong, Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12(5), 2179–2186 (2012). https://doi.org/10.1021/nl201040y
  53. C. Ríos, M. Stegmaier, P. Hosseini, D. Wang, T. Scherer et al., Integrated all-photonic non-volatile multi-level memory. Nat. Photonics 9(11), 725–732 (2015). https://doi.org/10.1038/nphoton.2015.182
  54. X. Zhang, W. Cai, M. Wang, B. Pan, K. Cao et al., Spin-torque memristors based on perpendicular magnetic tunnel junctions for neuromorphic computing. Adv. Sci. 8(10), 2004645 (2021). https://doi.org/10.1002/advs.202004645
  55. L. Liu, D. Wang, D. Wang, Y. Sun, H. Lin et al., Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware. Nat. Commun. 15(1), 4534 (2024). https://doi.org/10.1038/s41467-024-48631-4
  56. S.A. Siddiqui, S. Dutta, A. Tang, L. Liu, C.A. Ross et al., Magnetic domain wall based synaptic and activation function generator for neuromorphic accelerators. Nano Lett. 20(2), 1033–1040 (2020). https://doi.org/10.1021/acs.nanolett.9b04200
  57. D. Wang, R. Tang, H. Lin, L. Liu, N. Xu et al., Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing. Nat. Commun. 14(1), 1068 (2023). https://doi.org/10.1038/s41467-023-36728-1
  58. H. Tan, S. Majumdar, Q. Qin, J. Lahtinen, S. van Dijken, Mimicking neurotransmitter release and long-term plasticity by oxygen vacancy migration in a tunnel junction memristor. Adv. Intell. Syst. 1(2), 1900036 (2019). https://doi.org/10.1002/aisy.201900036
  59. J. Woo, X. Peng, S. Yu, Design considerations of selector device in cross-point RRAM array for neuromorphic computing. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). May 27-30, 2018, Florence, Italy. IEEE, (2018)., pp. 1–4.
  60. L. Shi, G. Zheng, B. Tian, B. Dkhil, C. Duan, Research progress on solutions to the sneak path issue in memristor crossbar arrays. Nanoscale Adv. 2(5), 1811–1827 (2020). https://doi.org/10.1039/d0na00100g
  61. H. Li, S. Wang, X. Zhang, W. Wang, R. Yang et al., Memristive crossbar arrays for storage and computing applications. Adv. Intell. Syst. 3(9), 2100017 (2021). https://doi.org/10.1002/aisy.202100017
  62. H. Zhao, Z. Liu, J. Tang, B. Gao, Q. Qin et al., Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis. Nat. Commun. 14, 2276 (2023). https://doi.org/10.1038/s41467-023-38021-7
  63. 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
  64. C. Li, D. Belkin, Y. Li, P. Yan, M. Hu et al., Efficient and self-adaptive in situ learning in multilayer memristor neural networks. Nat. Commun. 9(1), 2385 (2018). https://doi.org/10.1038/s41467-018-04484-2
  65. Y. Li, W. Song, Z. Wang, H. Jiang, P. Yan et al., Memristive field-programmable analog arrays for analog computing. Adv. Mater. 35(37), 2206648 (2023). https://doi.org/10.1002/adma.202206648
  66. C. Du, F. Cai, M.A. Zidan, W. Ma, S.H. Lee et al., Reservoir computing using dynamic memristors for temporal information processing. Nat. Commun. 8(1), 2204 (2017). https://doi.org/10.1038/s41467-017-02337-y
  67. F. Merrikh Bayat, M. Prezioso, B. Chakrabarti, H. Nili, I. Kataeva et al., Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat. Commun. 9(1), 2331 (2018). https://doi.org/10.1038/s41467-018-04482-4
  68. R. Athle, M. Borg, Ferroelectric tunnel junction memristors for in-memory computing accelerators. Adv. Intell. Syst. 6(3), 2300554 (2024). https://doi.org/10.1002/aisy.202300554
  69. T. Yu, F. He, J. Zhao, Z. Zhou, J. Chang et al., Hf0.5Zr0.5O2- based ferroelectric memristor with multilevel storage potential and artificial synaptic plasticity. Sci. China Mater. 64(3), 727–738 (2021). https://doi.org/10.1007/s40843-020-1444-1
  70. W. Wang, H. Zhao, B. Zhang, H. Tu, Forming-free Pt/Ti/AlOx/CeOx/Pt multilayer memristors with multistate and synaptic characteristics. J. Nanomater. 2022(1), 1370919 (2022). https://doi.org/10.1155/2022/1370919
  71. Z. Wu, Y. Zhang, S. Du, Z. Guo, W. Zhao, A three-valued adder circuit implemented in ZnO memristor with multi-resistance states. 2021 IEEE 14th International Conference on ASIC (ASICON). October 26–29, 2021. Kunming, China. IEEE, (2021). 1–3. https://doi.org/10.1109/asicon52560.2021.9620275
  72. C. Mahata, M. Kang, S. Kim, Multi-level analog resistive switching characteristics in tri-layer HfO2/Al2O3/HfO2 based memristor on ITO electrode. Nanomaterials 10(10), 2069 (2020). https://doi.org/10.3390/nano10102069
  73. S. Chen, H. Chen, Y. Lai, Reproducible non-volatile multi-state storage and emulation of synaptic plasticity based on a copper-nanop-embedded HfO x/ZnO bilayer with ultralow-switching current and ideal data retention. Nanomaterials 12(21), 3769 (2022). https://doi.org/10.3390/nano12213769
  74. J. Gosai, M. Patel, L. Liu, A. Lokhandwala, P. Thakkar et al., Control-etched Ti3C2Tx MXene nanosheets for a low-voltage-operating flexible memristor for efficient neuromorphic computation. ACS Appl. Mater. Interfaces 16(14), 17821–17831 (2024). https://doi.org/10.1021/acsami.4c01364
  75. L. Chen, Flexible 3D memristor array for binary storage and multi-states neuromorphic computing applications. InfoMat 3(2), 212–221 (2021). https://doi.org/10.1002/inf2.12158
  76. T.-Y. Wang, J.-L. Meng, Q.-X. Li, L. Chen, H. Zhu et al., Forming-free flexible memristor with multilevel storage for neuromorphic computing by full PVD technique. J. Mater. Sci. Technol. 60, 21–26 (2021). https://doi.org/10.1016/j.jmst.2020.04.059
  77. J.L. Meng, T.Y. Wang, Z.Y. He, L. Chen, H. Zhu et al., Flexible boron nitride-based memristor for in situ digital and analogue neuromorphic computing applications. Mater. Horizons 8(2), 538–546 (2021). https://doi.org/10.1039/d0mh01730b
  78. C. Lu, J. Meng, J. Song, T. Wang, H. Zhu et al., Self-rectifying all-optical modulated optoelectronic multistates memristor crossbar array for neuromorphic computing. Nano Lett. 24(5), 1667–1672 (2024). https://doi.org/10.1021/acs.nanolett.3c04358
  79. 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
  80. R. Zhao, M. He, L. Wang, Z. Chen, X. Cheng et al., Improved multilevel storage capacity in Ge2Sb2Te5-based phase-change memory using a high-aspect-ratio lateral structure. Sci. China Mater. 65(10), 2818–2825 (2022). https://doi.org/10.1007/s40843-022-2028-7
  81. B. Liu, K. Li, W. Liu, J. Zhou, L. Wu et al., Multi-level phase-change memory with ultralow power consumption and resistance drift. Sci. Bull. 66(21), 2217–2224 (2021). https://doi.org/10.1016/j.scib.2021.07.018
  82. N. Papandreou, A. Pantazi, A. Sebastian, M. Breitwisch, C. Lam et al., Multilevel phase-change memory. 2010 17th IEEE International Conference on Electronics, Circuits and Systems. December 12–15, 2010, Athens, Greece. IEEE, (2010)., pp. 1017–1020.
  83. F. Risch, A. Gilani, S. Kamaei, A.M. Ionescu, I. Stolichnov, Graphene-enhanced ferroelectric domain wall high-output memristor. Appl. Phys. Lett. 125(15), 152902 (2024). https://doi.org/10.1063/5.0232620
  84. J. Niu, Z. Fang, G. Liu, Z. Zhao, X. Yan, Multilevel state ferroelectric La: HfO2-based memristors and their implementations in associative learning circuit and face recognition. Sci. China Mater. 66(3), 1148–1156 (2023). https://doi.org/10.1007/s40843-022-2237-2
  85. J. Borghetti, G.S. Snider, P.J. Kuekes, J. Joshua Yang, D.R. Stewart et al., ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature 464(7290), 873–876 (2010). https://doi.org/10.1038/nature08940
  86. S. Kvatinsky, D. Belousov, S. Liman, G. Satat, N. Wald et al., MAGIC: memristor-aided logic. IEEE Trans. Circuits Syst. II Express Briefs 61(11), 895–899 (2014). https://doi.org/10.1109/TCSII.2014.2357292
  87. P. Huang, J. Kang, Y. Zhao, S. Chen, R. Han et al., Reconfigurable nonvolatile logic operations in resistance switching crossbar array for large-scale circuits. Adv. Mater. 28(44), 9758–9764 (2016). https://doi.org/10.1002/adma.201602418
  88. L. Luo, Z. Dong, S. Duan, C.S. Lai, Memristor-based stateful logic gates for multi-functional logic circuit. IET Circuits Devices Syst. 14(6), 811–818 (2020). https://doi.org/10.1049/iet-cds.2019.0422
  89. E. Linn, R. Rosezin, S. Tappertzhofen, U. Böttger, R. Waser, Beyond von Neumann: logic operations in passive crossbar arrays alongside memory operations. Nanotechnology 23(30), 305205 (2012). https://doi.org/10.1088/0957-4484/23/30/305205
  90. L. Liu, Y. Li, X. Huang, J. Chen, Z. Yang et al., Low-power memristive logic device enabled by controllable oxidation of 2D HfSe2 for in-memory computing. Adv. Sci. 8(15), e2005038 (2021). https://doi.org/10.1002/advs.202005038
  91. K. Raab, M.A. Brems, G. Beneke, T. Dohi, J. Rothörl et al., Brownian reservoir computing realized using geometrically confined skyrmion dynamics. Nat. Commun. 13(1), 6982 (2022). https://doi.org/10.1038/s41467-022-34309-2
  92. W. Kho, H. Hwang, S.E. Ahn, Exploring multi-bit logic in-memory with memristive HfO2-based ferroelectric tunnel junctions. Adv. Electron. Mater. 10(3), 2300618 (2024). https://doi.org/10.1002/aelm.202300618
  93. X. Xu, X. Zhou, T. Wang, X. Shi, Y. Liu et al., Robust DNA-bridged memristor for textile chips. Angew. Chem. Int. Ed. 59(31), 12762–12768 (2020). https://doi.org/10.1002/anie.202004333
  94. A.M. Hajisadeghi, H.R. Zarandi, M. Momtazpour, Stoch-IMC: A bit-parallel stochastic in-memory computing architecture based on STT-MRAM. AEU-Inter. J. Electron. Commun. 190, 155614 (2025). https://doi.org/10.1016/j.aeue.2024.155614
  95. Y. Sun, H. Wang, D. Xie, Recent advance in synaptic plasticity modulation techniques for neuromorphic applications. Nano-Micro Lett. 16(1), 211 (2024). https://doi.org/10.1007/s40820-024-01445-x
  96. Z. Yang, Z. R. Zhang, C. Li, D. L. Yang et al., Probing switching mechanism of memristor for neuromorphic computing. Nano Express. 4(2), (2023). https://doi.org/10.1088/2632-959X/acd70c
  97. H. Hong, X. Chen, W. Cho, H. Y. Yoo et al., Dynamic Convolutional Neural Networks Based on Adaptive 2D Memristors. Advanced Functional Materials. (2024). https://doi.org/10.1002/adfm.202422321
  98. L.-G. Wang, W. Zhang, Y. Chen, Y.-Q. Cao, A.-D. Li et al., Synaptic plasticity and learning behaviors mimicked in single inorganic synapses of Pt/HfOx/ZnOx/TiN memristive system. Nanoscale Res. Lett. 12(1), 65 (2017). https://doi.org/10.1186/s11671-017-1847-9
  99. X. Yan, J. Sun, Y. Zhang, Z. Zhao, L. Wang et al., An artificial synapse based on La: BiFeO3 ferroelectric memristor for pain perceptual nociceptor emulation. Mater. Today Nano 22, 100343 (2023). https://doi.org/10.1016/j.mtnano.2023.100343
  100. L.Z.O.L, Book review: the organization of behaviour: a neuropsychological theory. Q. J. Exp. Psychol. 2(3), 142–143 (1950). https://doi.org/10.1080/17470215008416589
  101. Z. L. Li, K. Y. Gao, Y. Y. Wang et al., Generation of an Ultra-Long Transverse Optical Needle Focus Using a Monolayer MoS2 Based Metalens. Advanced Optical Materials. 13(1), (2025). https://doi.org/10.1002/adom.202402024
  102. L. Sun, Z. Wang, J. Jiang, Y. Kim, B. Joo et al., In-sensor reservoir computing for language learning via two-dimensional memristors. Sci. Adv. 7(20), eabg1455 (2021). https://doi.org/10.1126/sciadv.abg1455
  103. C. Li, X. Chen, Z. R. Zhang et al., Charge-Selective 2D Heterointerface-Driven Multifunctional Floating Gate Memory for In Situ Sensing-Memory-Computing. Nano Letters. 24(47), 15025-15034 (2024). https://doi.org/10.1021/acs.nanolett.4c03828
  104. Y. Zhu, C. Wu, Z. Xu, Y. Liu, H. Hu et al., Light-emitting memristors for optoelectronic artificial efferent nerve. Nano Lett. 21(14), 6087–6094 (2021). https://doi.org/10.1021/acs.nanolett.1c01482
  105. M. Xu, X. Mai, J. Lin, W. Zhang, Y. Li et al., Recent advances on neuromorphic devices based on chalcogenide phase-change materials. Adv. Funct. Mater. 30(50), 2003419 (2020). https://doi.org/10.1002/adfm.202003419
  106. Q. Zhang, Y. Lu, Tunable optical power splitter based on directional coupler structure with phase change material Sb2Se3. Opt. Commun. 554, 130130 (2024). https://doi.org/10.1016/j.optcom.2023.130130
  107. A.H.A. Nohoji, P. Keshavarzi, M. Danaie, A photonic crystal waveguide intersection using phase change material for optical neuromorphic synapses. Opt. Mater. 151, 115372 (2024). https://doi.org/10.1016/j.optmat.2024.115372
  108. 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
  109. T.-Y. Wang, Z.-Y. He, H. Liu, L. Chen, H. Zhu et al., Flexible electronic synapses for face recognition application with multimodulated conductance states. ACS Appl. Mater. Interfaces 10(43), 37345–37352 (2018). https://doi.org/10.1021/acsami.8b16841
  110. X. Chen, D. L. Yang, G. Hwang, Y. J. Dong et al., Oscillatory neural network-based Ising machine using 2D memristors. Acs Nano 18(16), 10758-10767 (2024). https://doi.org/10.1021/acsnano.3c10559
  111. Y. N. Lin, X. Chen, Q. Y. Zhang, J. Q. You et al., Nano device fabrication for in-memory and in-sensor reservoir computing. International Journal of Extreme Manufacturing. 7(1), (2025). https://doi.org/10.1088/2631-7990/ad88bb
  112. J. Guckenheimer, R.A. Oliva, Chaos in the Hodgkin: Huxley model. SIAM J. Appl. Dyn. Syst. 1(1), 105–114 (2002). https://doi.org/10.1137/s1111111101394040
  113. D. Tal, E.L. Schwartz, Computing with the leaky integrate-and-fire neuron: logarithmic computation and multiplication. Neural Comput. 9(2), 305–318 (1997). https://doi.org/10.1162/neco.1997.9.2.305
  114. E. Izhikevich, R. FitzHugh, FitzHugh-nagumo model. Scholarpedia 1(9), 1349 (2006). https://doi.org/10.4249/scholarpedia.1349
  115. H. Lecar, Morris-lecar model. Scholarpedia 2(10), 1333 (2007). https://doi.org/10.4249/scholarpedia.1333
  116. B. Gutkin, in Theta-Neuron Model. ed.by JAEGER D, JUNG R (Springer New York; New York, NY, 2013), pp. 1–9.
  117. Z. P. Kilpatrick. in Wilson-Cowan Model. ed.by JAEGER D, JUNG R (Springer New York; New York, NY, 2013), pp. 1–5.
  118. N. Brunel, Modeling point neurons: from hodgkin-huxley to integrate-and-fire, in Erik De Schutter (ed.), Computational Modeling Methods for Neuroscientists (Cambridge, MA, 2009; online edn, MIT Press Scholarship Online, 22 Aug. 2013). https://doi.org/10.7551/mitpress/9780262013277.003.0008
  119. R. Fitzhugh, Thresholds and plateaus in the Hodgkin-Huxley nerve equations. J. Gen. Physiol. 43(5), 867–896 (1960). https://doi.org/10.1085/jgp.43.5.867
  120. D. Noble, Applications of Hodgkin-Huxley equations to excitable tissues. Physiol. Rev. 46(1), 1–50 (1966). https://doi.org/10.1152/physrev.1966.46.1.1
  121. Y.H. Liu, X.J. Wang, Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. J. Comput. Neurosci. 10, 25–45 (2001). https://doi.org/10.1023/A:1008916026143
  122. P. Stoliar, J. Tranchant, B. Corraze, E. Janod, M.P. Besland et al., A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv. Funct. Mater. 27(11), 1604740 (2017). https://doi.org/10.1002/adfm.201604740
  123. W. Teka, T.M. Marinov, F. Santamaria, Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model. PLoS Comput. Biol. 10(3), e1003526 (2014). https://doi.org/10.1371/journal.pcbi.1003526
  124. 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
  125. Y. Zhang, W. He, Y. Wu, K. Huang, Y. Shen et al., Highly compact artificial memristive neuron with low energy consumption. Small 14(51), e1802188 (2018). https://doi.org/10.1002/smll.201802188
  126. Y. Xu, S. Gao, Z. Li, R. Yang, X. Miao, Adaptive Hodgkin-Huxley neuron for retina-inspired perception. Adv. Intell. Syst. 4(12), 2200210 (2022). https://doi.org/10.1002/aisy.202200210
  127. X. Zhang, Y. Zhuo, Q. Luo, Z. Wu, R. Midya et al., An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11(1), 51 (2020). https://doi.org/10.1038/s41467-019-13827-6
  128. Y. Yang, F. Zhu, X. Zhang, P. Chen, Y. Wang et al., Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance. Nat. Commun. 15(1), 4318 (2024). https://doi.org/10.1038/s41467-024-48399-7
  129. M. Jung, S. Kim, J. Hwang, C. Kim, H.J. Kim et al., Monolithic three-dimensional Hafnia-based artificial nerve system. Nano Energy 126, 109643 (2024). https://doi.org/10.1016/j.nanoen.2024.109643
  130. C. Cui, S. Liu, J. Kwon, J.A.C. Incorvia, Spintronic artificial neurons showing integrate-and-fire behavior with reliable cycling operation. Nano Lett. 25(1), 361–367 (2025). https://doi.org/10.1021/acs.nanolett.4c05063
  131. J. Meng, J. Song, Y. Fang, T. Wang, H. Zhu et al., Ionic diffusive nanomemristors with dendritic competition and cooperation functions for ultralow voltage neuromorphic computing. ACS Nano 18(12), 9150–9159 (2024). https://doi.org/10.1021/acsnano.4c00424
  132. D.-H. Lim, S. Wu, R. Zhao, J.-H. Lee, H. Jeong et al., Spontaneous sparse learning for PCM-based memristor neural networks. Nat. Commun. 12, 319 (2021). https://doi.org/10.1038/s41467-020-20519-z
  133. T. Sun, B. Feng, J. Huo, Y. Xiao, W. Wang et al., Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett. 16(1), 14 (2023). https://doi.org/10.1007/s40820-023-01235-x
  134. Y. Dong, W. An, Z. Wang, D. Zhang, An artificial intelligence-assisted flexible and wearable mechanoluminescent strain sensor system. Nano-Micro Lett. 17(1), 62 (2024). https://doi.org/10.1007/s40820-024-01572-5
  135. R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14(1), 139 (2022). https://doi.org/10.1007/s40820-022-00887-5
  136. X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14(1), 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
  137. Z. Zhao, J. Tang, J. Yuan, Y. Li, Y. Dai et al., Large-scale integrated flexible tactile sensor array for sensitive smart robotic touch. ACS Nano 16(10), 16784–16795 (2022). https://doi.org/10.1021/acsnano.2c06432
  138. Y. Li, S. Chen, Z. Yu, S. Li, Y. Xiong et al., In-memory computing using memristor arrays with ultrathin 2D PdSeOx/PdSe2 heterostructure. Adv. Mater. 34(26), e2201488 (2022). https://doi.org/10.1002/adma.202201488
  139. D. Lee, M. Park, Y. Baek, B. Bae, J. Heo et al., In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat. Commun. 13(1), 5223 (2022). https://doi.org/10.1038/s41467-022-32790-3
  140. M. Ji, L. Yang, M. Pan, X. Zhang, J. Wang et al., In-sensor nonlinear convolutional processing based on hybrid MTJ/CMOS arrays. Digit. Signal Process. 147, 104412 (2024). https://doi.org/10.1016/j.dsp.2024.104412
  141. P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang et al., Fully hardware-implemented memristor convolutional neural network. Nature 577(7792), 641–646 (2020). https://doi.org/10.1038/s41586-020-1942-4
  142. S. Lee, G. An, G. Kim, S. Kim, Physical reservoir computing-based online learning of HfSiOx ferroelectric tunnel junction devices for image identification. Appl. Surf. Sci. 689, 162459 (2025). https://doi.org/10.1016/j.apsusc.2025.162459
  143. W. Song, M. Rao, Y. Li, C. Li, Y. Zhuo et al., Programming memristor arrays with arbitrarily high precision for analog computing. Science 383(6685), 903–910 (2024). https://doi.org/10.1126/science.adi9405
  144. B. Neupane, J. Aryal, A. Rajabifard, CNNs for remote extraction of urban features: a survey-driven benchmarking. Expert Syst. Appl. 255, 124751 (2024). https://doi.org/10.1016/j.eswa.2024.124751
  145. C.Y. Han, S.L. Fang, Y.L. Cui, W.H. Liu, S.Q. Fan et al., Configurable NbOx memristors as artificial synapses or neurons achieved by regulating the forming compliance current for the spiking neural network. Adv. Electron. Mater. 9(6), 2300018 (2023). https://doi.org/10.1002/aelm.202300018
  146. J. Jeong, Y. Jang, M.G. Kang, S. Hwang, J. Park et al., Spintronic artificial synapses using voltage-controlled multilevel magnetic states. Adv. Electron. Mater. 10(8), 2300889 (2024). https://doi.org/10.1002/aelm.202300889
  147. J.H. Quintino Palhares, N. Garg, P.-A. Mouny, Y. Beilliard, J. Sandrini et al., 28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs. NPJ Unconv. Comput. 1, 8 (2024). https://doi.org/10.1038/s44335-024-00008-y
  148. W.H. Cheong, J.B. Jeon, J.H. In, G. Kim, H. Song et al., Demonstration of neuromodulation-inspired stashing system for energy-efficient learning of spiking neural network using a self-rectifying memristor array (adv. Funct. Mater. 29/2022). Adv. Funct. Mater. 32(29), 2270169 (2022). https://doi.org/10.1002/adfm.202270169
Read More
Author biographies are not available.
Download this PDF file
PDF
Statistic
Read Counter : 427 Download : 324

Most read articles by the same author(s)