Keywords
Mott insulator
metal-insulator transition
vanadium dioxide
neuristor
synaptor
How to Cite
Downloads
Societal impact
Abstract
The escalating energy demands of modern artificial intelligence have exposed fundamental limitations in conventional computing architectures. Neuromorphic computing, which seeks to emulate the brain’s massively parallel and energy-efficient information processing, offers a compelling alternative, but its realization requires materials that naturally exhibit nonlinear, multi-state, and adaptive behavior characteristic of biological neurons and synapses. This review argues that strongly correlated electron materials, particularly Mott insulators, provide exactly this physical substrate. We present the essential physics of metal-insulator transitions in vanadium oxides (VO2, V2O3) and perovskite manganites in an accessible manner and describe how their switching behaviors map onto neuromorphic primitives: the neuristor (artificial neuron) and the synaptor (artificial synapse). The review draws extensively on the authors’ experimental work: direct imaging of nanotextured phase coexistence in V2O3; an MIT Fingerprinting approach to engineer multi-state resistive switching in VO2; the demonstration of subthreshold firing in Mott nanodevices; and electrically controlled multi-state analog memory in phase-separated manganites. The review concludes by identifying the main challenges and the most promising directions to translate these laboratory demonstrations into practical brain-inspired computing systems.
References
Attwell, D., Laughlin, S. B. (2001) An energy budget for signaling in the grey matter of the brain. Journal of Cerebral Blood Flow & Metabolism, 21(10), 1133–1145. https://doi.org/10.1097/00004647-200110000-00001
Cao, J., Zhang, X., Cheng, H., Qiu, J., Liu, X., Wang, M., Liu, Q. (2022) Emerging dynamic memristors for neuromorphic reservoir computing. Nanoscale, 14, 289–298. https://doi.org/10.1039/D1NR06680C
Cardona-Rodríguez, A., Arango, I. C., Gomez, M. F., Dominguez, C., Trastoy, J., Urban, C., Sulekar, S., Nino, J. C., Schuller, I. K., Gomez, M. E. (2019) Resistive switching in multiferroic BiFeO3 films: Ferroelectricity versus vacancy migration. Solid State Communications, 288, 38–42. https://doi.org/10.1016/j.ssc.2018.11.005
Carranza-Celis, D., Cardona-Rodríguez, A., Narváez, J., Moscoso-Londoño, O., Muraca, D., Knobel, M., Ornelas-Soto, N., Reiber, A., Ramírez, J. G. (2019) Control of multiferroic properties in BiFeO3 nanoparticles. Scientific Reports, 9, 3182. https://doi.org/10.1038/s41598-019-39517-3
Carranza-Celis, D., Skoropata, E., Biswas, A., Fitzsimmons, M. R., Schuller, I. K., Ramirez, J. G. (2021) Magnetism dynamics driven by phase separation in pr-doped manganite thin films: A ferromagnetic resonance study. Physical Review Materials, 5(12). https://doi.org/10.1103/physrevmaterials.5.124413
Carranza-Celis, D., Salev, P., Basaran, A. C., Schuller, I. K., Ramírez, J. G. (2025) Electronic and magnetic memory enabled by phase separation [Manuscript in preparation].
Ceballos Medina, S., Marín Mercado, L., Cardona-Rodríguez, A., Quiñonez Penagos, M. F., Magén, C., Rodríguez, L. A., Ramírez, J. G. (2025) Resistive switching mechanisms in BiFeO3 devices with YBCO and Ag as top electrodes. Physics Open, 22, 100249. https://doi.org/10.1016/j.physo.2024.100249
Cheng, S., Navarro, H., Wang, Z., Li, X., Kaur, J., Pofelski, A., Meng, Q., Zhou, C., Chen, C., Dean, M. P. M., Liu, M., Basaran, A. C., Rozenberg, M., Ong, S. P., Schuller, I. K., & Zhu, Y. (2025) Purely electronic insulator-metal transition in rutile vo2. Nature Communications, 16, 5444. https://doi.org/10.1038/s41467-025-6 0243-0
Corti, E., Cornejo Jimenez, J. A., Niang, K. M., Robertson, J., Moselund, K. E., Gotsmann, B., Ionescu, A. M., Karg, S. (2021) Coupled VO2 oscillators circuit as analog first layer filter in convolutional neural networks. Frontiers in Neuroscience, 15, 628254. https://doi.org/10.3389/fnins.2021.628254
Corti, E., Gotsmann, B., Moselund, K., Stolichnov, I., Ionescu, A., Karg, S. (2018) Resistive coupled VO2 oscillators for image recognition. 2018 IEEE International Conference on Rebooting Computing (ICRC), 1–7. https://doi.org/10.1109/icrc.2018.8638626
Crane, H. D. (1962) Neuristor—a novel device and system concept. Proceedings of the IRE, 50(10), 2048–2060. https://doi.org/10.1109/JRPROC.1962.288130
Dagotto, E., Hotta, T., Moreo, A. (2001) Colossal magnetoresistant materials: The key role of phase separation. Physics Reports, 344(1–3), 1–153. https://doi.org/10.1016/S0370-1573(00)00121-6
Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., Liao, Y., Lin, C.-K., Lines, A., Liu, R., Mathaikutty, D., McCoy, S., Paul, A., Tse, J., Venkataramanan, G., . . . Wang, H. (2018) Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1), 82–99. https://doi.org/10.1109/MM.2018.112130359
Delacour, C., Todri-Sanial, A. (2021) Mapping Hebbian learning rules to coupling resistances for oscillatory neural networks. Frontiers in Neuroscience, 15, 694549. https://doi.org/10.3389/fnins.2021.694549
del Valle, J., Ramírez, J. G., Rozenberg, M. J., Schuller, I. K. (2018) Challenges in materials and devices for resistive-switching-based neuromorphic computing. Journal of Applied Physics, 124(21), 211101. https://doi.org/10.1063/1.5047800
del Valle, J., Ramírez, J. G., Rozenberg, M. J., Schuller, I. K. (2019) Subthreshold firing in Mott nanodevices. Nature, 569, 388–392. https://doi.org/10.1038/s41586-019-1159-6
del Valle, J., Vargas, N. M., Rocco, R., Salev, P., Kalcheim, Y., Lapa, P. N., Adda, C., Lee, M.-H., Wang, P. Y., Fratino, L., Rozenberg, M. J., Schuller, I. K. (2021) Spatiotemporal characterization of the field-induced insulator-to-metal transition. Science, 373(6557), 907–911. https://doi.org/10.1126/science.abd9088
Deng, S., Yu, H., Park, T. J., Islam, A. N. M. N., Manna, S., Pofelski, A., Wang, Q., Zhu, Y., Sankaranarayanan, S. K. R. S., Sengupta, A., Ramanathan, S. (2023) Selective area doping for Mott neuromorphic electronics. Science Advances, 9(11), eade4838. https://doi.org/10.1126/sciadv.ade4838
Gomide, G. B., Carranza-Celis, D., Kuhl, G., Knobel, M., Ramírez, J. G., Muraca, D. (2025) Voltage-tunable spin resonance in quantum phase-separated material. APL Materials, 13(4), 041122. https://doi.org/10.1063/5.0256253
Goodenough, J. B. (1971) The two components of the crystallographic transition in VO2. Journal of Solid State Chemistry, 3(4), 490–500. https://doi.org/10.1016/0022-4596(71)90091-0
Guénon, S., Scharinger, S., Wang, S., Ramírez, J. G., Koelle, D., Kleiner, R., Schuller, I. K. (2013) Electrical breakdown in a V2O3 device at the insulator-to-metal transition. EPL (Europhysics Letters), 101(5), 57003. https://doi.org/10.1209/0295-5075/101/57003
Herculano-Houzel, S. (2009) The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31. https://doi.org/10.3389/neuro.09.031.2009
Imada, M., Fujimori, A., Tokura, Y. (1998) Metal-insulator transitions. Reviews of Modern Physics, 70(4), 1039–1263. https://doi.org/10.1103/RevModPhys.70.1039
Jiménez, M., Núñez, J., Shamsi, J., Linares-Barranco, B., Avedillo, M. J. (2023) Experimental demonstration of coupled differential oscillator networks for versatile applications. Frontiers in Neuroscience, 17, 1294954. https://doi.org/10.3389/fnins.2023.1294954
Joushaghani, A., Jeong, J., Paradis, S., Alain, D., Aitchison, J. S., Poon, J. K. S. (2014) Wavelength-size hybrid Si-VO2 waveguide electroabsorption optical switches and photodetectors. Optics Express, 22(3), 3968–3976. https://doi.org/10.1364/OE.22.003968
Kisiel, E., Salev, P., Poudyal, I., Alspaugh, D. J., Carneiro, F., Qiu, E., Rodolakis, F., Zhang, Z., Shpyrko, O. G., Rozenberg, M., Schuller, I. K., Islam, Z., Frano, A. (2025) High-resolution full-field structural microscopy of the voltage-induced filament formation in vo2-based neuromorphic devices. ACS Nano, 19(16), 15385–15394. https://doi.org/10.1021/acsnano.4c14696
Kudithipudi, D., Saleh, Q., Merkel, C., Thesing, J., Wysocki, B. (2016) Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in Neuroscience, 9, 502. https://doi.org/10.3389/fnins.2015.00502
Kumar, S., Strachan, J. P., Pickett, M. D., Bratkovsky, A., Nishi, Y., Williams, R. S. (2014) Synchronized charge oscillations in correlated electron systems. Scientific Reports, 4, 4964. https://doi.org/10.1038/srep04964
Kunwar, S., Cucciniello, N. P., Mazza, A. R., Zhang, D., Santillan, L., Freiman, B., Roy, P., Jia, Q., MacManus-Driscoll, J. L., Wang, H., Nie, W., Chen, A. (2024) Reconfigurable resistive switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) memristive devices for neuromorphic computing. ACS Applied Materials & Interfaces, 16(15), 19103–19111. https://doi.org/10.1021/acsami.3c19032
Laad, M. S., Craco, L. (2024) Mott transitions: A brief review. Advanced Quantum Technologies, 7, 2200186. https://doi.org/10.1002/qute.202200186
Lie, S. (2021) Cerebras CS-2: Architecture and performance of a wafer-scale AI accelerator [2.6 trillion transistors, TSMC 7 nm, system power 23 kW]. IEEE Hot Chips 33 Symposium (HCS). https://doi.org/10.1109/HCS52781.2021.9567183
Liu, Z., Zhang, Q., Xie, D., Zhang, M., Li, X., Zhong, H., Li, G., He, M., Shang, D., Wang, C., Gu, L., Yang, G., Jin, K., Ge, C. (2023) Interface-type tunable oxygen ion dynamics for physical reservoir computing. Nature Communications, 14, 6612. https://doi.org/10.1038/s41467-023-42993-x
McLeod, A. S., van Heumen, E., Ramírez, J. G., Wang, S., Saerbeck, T., Guenon, S., Goldflam, M., Anderegg, L., Kelly, P., Mueller, A., Liu, M. K., Schuller, I. K., Basov, D. N. (2017) Nanotextured phase coexistence in the correlated insulator V2O3. Nature Physics, 13, 80–86. https://doi.org/10.1038/nphys3882
Mead, C. (1989) Analog VLSI and neural systems. Addison-Wesley.
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S. K., Appuswamy, R., Taba, B., Amir, A., Flickner, M. D., Risk, W. P., Manohar, R., Modha, D. S. (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668–673. https://doi.org/10.1126/science.1254642
Morrison, V. R., Chatelain, R. P., Tiwari, K. L., Hendaoui, A., Bruhács, A., Chaker, M., Siwick, B. J. (2014) A photoinduced metal-like phase of monoclinic VO2 revealed by ultrafast electron diffraction. Science, 346(6208), 445-448. https://doi .org/10.1126/science.1253779
Núñez, J., Avedillo, M. J., Jiménez, M., Quintana, J. M., Todri-Sanial, A., Corti, E., Karg, S., Linares-Barranco, B. (2021) Oscillatory neural networks using VO2 based phase encoded logic. Frontiers in Neuroscience, 15, 655823. https://doi.org/10.3389/fnins.2021.655823
Oh, S., Shi, Y., del Valle, J., Salev, P., Lu, Y., Huang, Z., Kalcheim, Y., Schuller, I. K., Kuzum, D. (2021) Energy-efficient mott activation neuron for full-hardware implementation of neural networks. Nature Nanotechnology, 16(6), 680–687. https://doi.org/10.1038/s41565-021-00874-8
Pickett, M. D., Medeiros-Ribeiro, G., Williams, R. S. (2013) A scalable neuristor built with Mott memristors. Nature Materials, 12(2), 114-117. https://doi.org/10.1038 /nmat3510
Pofelski, A., Liu, C., Reisbick, S. A., Han, M.-G., Wu, L., Navarro, H., Qiu, E., Wang, T. D., Mousavi M., S. S., Alspaugh, D. J., Rozenberg, M., Ramanathan, S., Schuller, I. K., & Zhu, Y. (2026) Switching speed limits in electrically driven vo2 structural mott–peierls transition. Nature Communications, 17, 3139. https://doi.org /10.1038/s41467-026-69904-0
Qazilbash, M. M., Brehm, M., Chae, B. G., Ho, P. C., Andreev, G. O., Kim, B. J., Yun, S. J., Balatsky, A. V., Maple, M. B., Keilmann, F., Kim, H. T., Basov, D. N. (2007) Mott transition in VO2 revealed by infrared spectroscopy and nano-imaging. Science, 318(5857), 1750–1753. https://doi.org/10.1126/science.1150124
Qiu, E., Zhang, Y.-H., Ventra, M. D., Schuller, I. K. (2023) Reconfigurable cascaded thermal neuristors for neuromorphic computing. Advanced Materials, 36(6), 2306818. https ://doi.org/10.1002/adma.202306818
Ramírez, J. G., Sharoni, A., Dubi, Y., Gómez, M. E., Schuller, I. K. (2009) First-order reversal curve measurements of the metal-insulator transition in VO2: Signatures of persistent metallic domains. Physical Review B, 79(23), 235110. https://doi.org/10.1103/PhysRevB.79.235110
Ramírez, J. G. (2026) Experimental deconvolution of electronic and thermal switching mechanisms in the VO2 Metal-Insulator Transition. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 50(194), 54-68. https://doi .org/10.18257/raccefyn.3273
Salev, P., Kisiel, E., Sasaki, D., Gunn, B., He, W., Feng, M., Li, J., Tamura, N., Poudyal, I., Islam, Z., Takamura, Y., Frano, A., Schuller, I. K. (2024) Local strain inhomogeneities during electrical triggering of a metal–insulator transition revealed by x-ray microscopy. Proceedings of the National Academy of Sciences, 121(34), e2317944121. https://doi.org/10.1073/pnas.2317944121
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., Eleftheriou, E. (2020) Memory devices and applications for in-memory computing. Nature Nanotechnology, 15(7), 529–544. https://doi.org/10.1038/s41565-020-0655-z
Seoane, L. F. (2019) Evolutionary aspects of reservoir computing. Philosophical Transactions of the Royal Society B, 374, 20180377. https://doi.org/10.1098/rstb.2018.0377
Sharoni, A., Ramírez, J. G., Schuller, I. K. (2008) Multiple avalanches across the metal-insulator transition of vanadium oxide nanoscaled junctions. Physical Review Letters, 101(2), 026404. https://doi.org/10.1103/PhysRevLett.101.026404
Stoliar, P., Tranchant, J., Corraze, B., Janod, E., Besland, M.-P., Tesler, F., Rozenberg, M., Cario, L. (2017) A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Advanced Functional Materials, 27(11), 1604740. https://doi.org/10.1002/adfm.201604740
Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., Hirose, A. (2019) Recent advances in physical reservoir computing: A review. Neural Networks, 115, 100-123. https://doi.org/10.1016/j.neunet.2019.03.005
Weber, C., O'Regan, D. D., Hine, N. D. M., Payne, M. C., Kotliar, G., & Littlewood, P. B. (2012) Vanadium dioxide: A Peierls-Mott insulator stable against disorder. Physical Review Letters, 108(25), 256402. https://doi.org/10.1103/PhysRevLett.108.256402
Torres, F., Basaran, A. C., Schuller, I. K. (2023) Thermal management in neuromorphic materials, devices, and networks. Advanced Materials, 35(37), 2205098. https://doi.org/10 .1002/adma.202205098
Valmianski, I., Wang, P. Y., Wang, S., Ramírez, J. G., Guénon, S., Schuller, I. K. (2018) Origin of the current-driven breakdown in vanadium oxides: Thermal versus electronic. Physical Review B, 98(19), 195144. https://doi.org/10.1103/PhysRevB.98.195144
Wang, S., Ramírez, J. G., Jeffet, J., Bar-Ad, S., Huppert, D., Schuller, I. K. (2017) Ultrafast photo-induced dynamics across the metal-insulator transition of VO2. EPL (Europhysics Letters), 118, 27005. https://doi.org/10.1209/0295-5075/118/27005
Wang, S., Ramírez, J. G., Schuller, I. K. (2015) Avalanches in vanadium sesquioxide nanodevices. Physical Review B, 92, 085150. https://doi.org/10.1103/PhysRevB.92.085150
Zimmers, A., Aigouy, L., Mortier, M., Sharoni, A., Wang, S., West, K. G., Ramírez, J. G., Schuller, I. K. (2013) Role of thermal heating on the voltage induced insulator-metal transition in VO2. Physical Review Letters, 110(5), 056601. https://doi.org/10.1103/PhysRevLett.110.056601
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales