Nanomaterial-based memristors for neuromorphic computing: Materials, device architectures, and emerging applications

Authors

https://doi.org/10.48314/nna.vi.71

Abstract

The increasing computational demands of Artificial Intelligence (AI) and data-intensive applications have stimulated the development of energy-efficient and highly integrated computing technologies. Neuromorphic computing has emerged as a promising alternative to conventional von Neumann architectures by integrating memory and computation and emulating key functionalities of biological neural systems. In this context, memristive devices have attracted significant attention because of their nanoscale dimensions, history-dependent resistance modulation, multilevel conductance states, and potential for implementing artificial synapses and neurons. The incorporation of nanomaterials has further expanded the capabilities of memristive devices by enabling tunable electrical, structural, and interfacial properties. This review provides an overview of recent advances in nanomaterial-based memristors for neuromorphic computing, with emphasis on the major classes of nanomaterials, including Two-dimensional (2D) materials, metal oxides, nanoparticles, quantum dots, nanocomposites, and emerging hybrid systems. Different device architectures and their roles in resistive switching, synaptic plasticity, multilevel operation, and in-memory computing are discussed. The emerging applications of nanomaterial-based memristors in Artificial Neural Networks (ANNs), edge AI, image and pattern recognition, signal processing, robotics, and Internet-of-Thing’s systems are also reviewed. In addition, key performance characteristics and technological challenges, including switching speed, energy consumption, endurance, retention, variability, scalability, and Complementary Metal-Oxide-Semiconductor (CMOS) compatibility, are critically discussed. Finally, emerging research directions involving 2D heterostructures, three-dimensional architectures, flexible electronics, and hardware-algorithm co-design are highlighted. This review provides an integrated perspective on the relationship between nanomaterial properties, memristive device architectures, and neuromorphic functionality, offering insights into the development of scalable and energy-efficient hardware for next-generation intelligent computing systems.

Keywords:

Nanomaterials, Neuromorphic computing, Resistive switching, Artificial synapses, Artificial intelligence, Edge computing, Nanotechnology

References

  1. [1] Lodeiro, C. (2026). Advanced nanomaterials and nanotechnology: Highlights from the 2025 editor’s choice collection. Materials, 19(11), 2392. https://doi.org/10.3390/ma19112392
  2. [2] Rathour, R. K., Sharma, N., Jain, K., Kanika, Singh, S., Sharma, A., & Bhatia, R. K. (2026). Nanotechnology advances in biomass-derived green nanomaterials for mitigating environmental toxicity. Discover Nano, 21(1), 394. https://doi.org/10.1186/s11671-026-04852-8
  3. [3] Singh, H., Dhanu, A. S., Joshi, A. S., Mijakovic, I., & Singh, P. (2026). Next-generation nanomaterials for environmental remediation: Smart design, hybrid materials and sustainable use. Frontiers in Chemistry, 14, 1772161. https://doi.org/10.3389/fchem.2026.1772161
  4. [4] Nag, A., & Chaudhuri, D. (2026). Advances in nanomaterials for energy and sensing applications: From fundamentals to device. Discover Electronics, 3(1), 13. https://doi.org/10.1007/s44291-026-00165-3
  5. [5] Shin, S. K., Sharma, N., An, S. S. A., & Kim, M. K. (2026). The evolution of nanoparticle regulation: A meta-analysis of research trends and historical parallels (2015–2025). Nanomaterials, 16(2), 134. https://doi.org/10.3390/nano16020134
  6. [6] Niknejad, K., Sharifzadeh, B. M., & Motallebi, T. T. S. (2018). Synthesis of Metformin Hydrochloride nanoliposomes: Evaluation of physicochemical characteristics and release kinetics. International Journal of Nano Dimension, 9(3), 298-313. (In Persian). https://www.sid.ir/paper/322408/en
  7. [7] Alhussaini, M. S., Alyahya, A. A. I., & Al-Ghanayem, A. A. (2026). TiO2-based nanomaterials as antimicrobial agents: Recent progress and trends (2020–2025). Main Group Chemistry, 25(3), 10241221251413990. https://doi.org/10.1177/10241221251413990
  8. [8] Atofarati, E. O., Ajuka, L. O., & Enweremadu, C. C. (2026). Life cycle assessment of nanomaterials and laboratory consumables: Toward safer and sustainable nanotechnology. Journal of Nanoparticle Research, 28(4), 94. https://doi.org/10.1007/s11051-026-06621-1
  9. [9] Sangwan, V. K., & Hersam, M. C. (2020). Neuromorphic nanoelectronic materials. Nature Nanotechnology, 15(7), 517–528. https://doi.org/10.1038/s41565-020-0647-z
  10. [10] Kwon, K. C., Baek, J. H., Hong, K., Kim, S. Y., & Jang, H. W. (2022). Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano-Micro Letters, 14(1), 58. https://doi.org/10.1007/s40820-021-00784-3
  11. [11] Sun, B., Guo, T., Zhou, G., Ranjan, S., Jiao, Y., Wei, L., ... & Wu, Y. A. (2021). Synaptic devices based neuromorphic computing applications in artificial intelligence. Materials Today Physics, 18, 100393. https://doi.org/10.1016/j.mtphys.2021.100393
  12. [12] Nirmal, K. A., Kumbhar, D. D., Kesavan, A. V., Dongale, T. D., & Kim, T. G. (2024). Advancements in 2D layered material memristors: Unleashing their potential beyond memory. Npj 2d Materials and Applications, 8(1), 83. https://www.nature.com/articles/s41699-024-00522-4
  13. [13] Grindrod, P. (2026). On the next generation for neuromorphic computing and neuromorphic AI. Philosophical Transactions of the Royal Society B: Biological Sciences, 384(2324), 20250122. https://doi.org/10.1098/rsta.2025.0122
  14. [14] Chen, G., Xu, M., Chen, Y., Yuan, F., Qin, L., & Ren, J. (2026). A new era in computing: A review of neuromorphic computing chip architecture and applications. Chips, 5(1), 3. https://doi.org/10.3390/chips5010003
  15. [15] Latif, S., Zafar, S., Ahmad, J., Khan, M. Z., Ullah, F., & Khattak, A. A. (2026). Neuromorphic computing for energy-efficient machine intelligence. Engineering Research Express, 8(3), 032202. https://doi.org/10.1088/2631-8695/ae1d0c
  16. [16] Arun Antony, V., & Muthukumaran, N. (2026). Neuromorphic computing architecture using multigate field effect transistor based inexact computing and synapse circuits for neurorehabilitation. IETE Journal of Research, 72(7), 1–9. https://doi.org/10.1080/03772063.2026.2624599
  17. [17] Fares, H., Ronchini, M., Zamani, M., Farkhani, H., Chiappalone, M., Neftci, E., & Moradi, F. (2026). Towards neuromorphic neurotechnologies: Integrating brain-inspired computing with brain-computer interfaces. Npj Biomedical Innovations, 3(1), 51. https://doi.org/10.1038/s44385-026-00074-w
  18. [18] Alinsaif, S. (2026). Neuromorphic computing for long-term cardiac health: A review of spiking neural networks in low-power wearable electronics. Electronics, 15(6), 1179. https://doi.org/10.3390/electronics15061179
  19. [19] Lu, L., Sun, B., Wang, Z., Meng, J., & Wang, T. (2026). Two-dimensional MXene-based advanced sensors for neuromorphic computing intelligent application. Nano-Micro Letters, 18(1), 64. https://doi.org/10.1007/s40820-025-01902-1
  20. [20] Cheng, Y., Wang, M., Hao, Z., & Buyya, R. (2026). Neuromorphic quantum computing: A survey and future directions in evolutionary and gradient-based learning strategies for quantum spiking neural networks. Neurocomputing, 701, 134580. https://doi.org/10.1016/j.neucom.2026.134580
  21. [21] Lan, D., Ma, B., Ji, Y., Zeng, Y., Zou, Z., Li, Z., ... & Zou, W. (2026). Toward brain-inspired intelligence: A review of photonic neuromorphic computing systems. PhotoniX, 7(1), 58. https://doi.org/10.1186/s43074-026-00278-8
  22. [22] Semerikov, S. O., Nechypurenko, P. P., Vakaliuk, T. A., Mintii, I. S., & Kolhatin, A. O. (2026). Neuromorphic computing for ultra-low latency cognitive radio: A paradigm shift toward brain-inspired spectrum intelligence. Discover Artificial Intelligence, 6, 363. https://doi.org/10.1007/s44163-026-01093-7
  23. [23] Kim, H., Sohn, W., Lee, J. H., Yang, S. J., Shim, Y. S., Moon, C. W., & Kim, H. (2026). Recent advances in MXene materials for resistive switching memory devices. Journal of the Korean Ceramic Society, 63(2), 171–187. https://doi.org/10.1007/s43207-025-00547-5
  24. [24] Qiao, X., Liu, Z., Sun, J., Yan, X., Jia, X., Jiao, J., ... & Zhang, Z. (2026). Resistive switching oxides: Mechanism, performance, and device‐algorithm co‐design for artificial intelligence. Advanced Materials, 38(29), e17373. https://doi.org/10.1002/adma.202517373
  25. [25] Normurodov, M. T., Ochilov, O., Yuldashev, O. Y., Karshieva, Z. A., & Toshboyev, N. U. (2026). Resistive switching behavior of Si/TiO thin films for non-volatile memory applications. East European Journal of Physics, (2), 275–279. https://doi.org/10.26565/2312-4334-2026-2-29
  26. [26] Namade, L. D., Patil, A. R., Bodake, T. R., Mohite, S. V., Dongale, T. D., Sutar, S. S., ... & Rajpure, K. Y. (2026). Electroforming− free resistive switching in spray− deposited SrTiO3 thin films with time series analysis for memory devices. Materials Research Bulletin, 202, 114197. https://doi.org/10.1016/j.materresbull.2026.114197
  27. [27] Kim, S., Yoon, C., Yim, H., Kim, T., Suh, H., Ryu, W., ... & Park, B. H. (2026). Dual bipolar resistive switching in wafer‐scalable 2D perovskite oxide nanosheets‐based memristor. Advanced Science, 13(11), e17588. https://doi.org/10.1002/advs.202517588
  28. [28] Yang, F., Sun, H., Zhang, X., Chen, D., Chen, J., Zhang, X., ... & Li, Y. (2026). Optoelectronic synaptic memristor with coupled reversible self‐rectifying and bipolar resistive switching for multifunctional neuromorphic applications. Advanced Functional Materials, 36(6), e16894. https://doi.org/10.1002/adfm.202516894
  29. [29] Matsukatova, A. N., Ryabova, M. A., Emelyanov, A. V., Trofimov, A. D., Patsaev, T. D., & Rylkov, V. V. (2026). Electrochemical origins underlying enhanced resistive switching reliability in flexible Cu/parylene-MoOx/ITO memristors for wearable neuromorphic applications. Journal of Alloys and Compounds, 1080, 190674. https://doi.org/10.1016/j.jallcom.2026.190674
  30. [30] Al‐Sulami, A. I., Al Sulami, F. M. H., Aldahiri, R. H., Adhikari, A., Khalil, R., & Hameed, T. A. (2026). Unveiling the resistive and capacitive switching memory in PVA doped with silver nanoparticles for wearable and solid‐state memory devices. Polymers for Advanced Technologies, 37(1), e70488. https://doi.org/10.1002/pat.70488
  31. [31] Pham, P. Q., Bao, T. D. N., Nasr, B., Yoshimura, M., & Pham, N. K. (2026). Interface-driven resistive switching and synaptic behavior in the graphene oxide-based memristive devices. Carbon, 251, 121316. https://doi.org/10.1016/j.carbon.2026.121316
  32. [32] Mukherjee, S., Ganaie, M. M., Chatterjee, A., Auti, C. B., Adamu, J., Das, C., ... & Kumar, M. (2026). Thermally enhanced resistive switching in interface-engineered AgHfO3− x/Al2O3 memristors via trap depopulation for neuromorphic computing. Materials Horizons. https://doi.org/10.1039/d6mh00879h
  33. [33] Uriarte, S., Baier-Fuentes, H., Espinoza-Benavides, J., & Inzunza-Mendoza, W. (2026). Artificial intelligence technologies and entrepreneurship: a hybrid literature review. Review of Managerial Science, 20(1), 251–299. https://doi.org/10.1007/s11846-025-00839-4
  34. [34] Verma, V., & Kumar, D. (2026). Artificial intelligence and machine learning in drug discovery: From lead discovery to clinical validation (2020–2025). Letters in Drug Design & Discovery, 22(12), 100341. https://doi.org/10.1016/j.lddd.2026.100341
  35. [35] Jaboob, M., Al-Ansi, A. M., & Riyadh, H. A. (2026). Artificial intelligence in higher education: A PRISMA 2020 review. International Journal of Educational Management, 4(1–2), 113–132. https://doi.org/10.1108/IJEM-09-2024-0560
  36. [36] Che Ghazali, R., Abdul Hanid, M. F., Mohd Said, M. N. H., & Lee, H. Y. (2026). The advancement of artificial intelligence in education: Insights from a 1976–2024 bibliometric analysis. Journal of Research on Technology in Education, 58(4), 906–922. https://doi.org/10.1080/15391523.2025.2456044
  37. [37] Sarun, H., Rotana, S., & Chhunla, C. (2026). The role and significance of artificial intelligence in transforming modern society: Opportunities, challenges, and future directions. Journal of Agriculture and Environment, 3(1), 133–141.
  38. [38] Zakarneh, B., Ahmed, Z. A., & Alrefaee, Y. M. H. A. (2026). Post-editing in the age of artificial intelligence: A bibliometric analysis of scopus-indexed journal articles (2018–May 2026). Cogent arts & Humanities, 13(1), 2719067. https://doi.org/10.1080/23311983.2026.2719067
  39. [39] Ye, K., & Zhang, L. J. (2025). Edge computing–EDGE 2025: 9th international conference, held as part of the services conference federation, SCF 2025, Hong Kong, China, September 27–30, 2025, Proceedings. Springer Nature. https://murderbooks.com/book/9783032063069
  40. [40] Mashmool, A., Delzanno, G., Saadatfar, H., Ahmad, A., Koschke, R., Alizadehsani, R., ... & D’Agostino, D. (2026). Edge computing in healthcare using machine learning: A systematic literature review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 16(1), e70069. https://doi.org/10.1002/widm.70069
  41. [41] Vali, A. A., Azizi, S., Shojafar, M., & Buyya, R. (2026). Energy-efficient resource management in microservices-based fog and edge computing: State-of-the-art and future directions. ACM Computing Surveys, 58(12), 1–42. https://doi.org/10.1145/3797911

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2026-09-11

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Vol. 1 No. 3 (2026): Nano Nexus & Applications

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How to Cite

Shafiee, S. A. H. . (2026). Nanomaterial-based memristors for neuromorphic computing: Materials, device architectures, and emerging applications. Nano Nexus & Applications, 1(3), 199-216. https://doi.org/10.48314/nna.vi.71

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