Jia, Z. et al. The importance of resource awareness in artificial intelligence for healthcare. Nat. Mach. Intell. 5, 687–698 (2023).
Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X. 2, 89–124 (2017).
Aguirre, F. et al. Hardware implementation of memristor-based artificial neural networks. Nat. Commun. 15, 1974 (2024).
Google Scholar
Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).
Google Scholar
Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).
Zhou, G. et al. Volatile and nonvolatile memristive devices for neuromorphic computing. Adv. Electron. Mater. 8, 2101127 (2022).
Google Scholar
Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nat. Rev. Mater. 7, 575–591 (2022).
Google Scholar
Sokolov, A. S., Abbas, H., Abbas, Y. & Choi, C. Towards engineering in memristors for emerging memory and neuromorphic computing: A review. J. Semicond. 42, 013101 (2021).
Zhang, X., Huang, A., Hu, Q., Xiao, Z. & Chu, P. K. Neuromorphic computing with memristor crossbar. Phys. Status Solidi A. 215, 1700875 (2018).
Google Scholar
Upadhyay, N. K. et al. Emerging memory devices for neuromorphic computing. Adv. Mater. Technol. 4, 1800589 (2019).
Thomas, A. Memristor-based neural networks. J. Phys. Appl. Phys. 46, 093001 (2013).
Google Scholar
Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020).
Google Scholar
Hong, X. et al. Oxide-based RRAM materials for neuromorphic computing. J. Mater. Sci. 53, 8720–8746 (2018).
Google Scholar
Moon, K. et al. RRAM-based synapse devices for neuromorphic systems. Faraday Discuss. 213, 421–451 (2019).
Google Scholar
Shen, Z. et al. Advances of RRAM devices: resistive switching mechanisms, materials and bionic synaptic application. Nanomaterials 10, 1437 (2020).
Google Scholar
Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).
Google Scholar
Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).
Google Scholar
Abbas, H. et al. The coexistence of threshold and memory switching characteristics of ALD HfO 2 memristor synaptic arrays for energy-efficient neuromorphic computing. Nanoscale 12, 14120–14134 (2020).
Google Scholar
John, R. A. et al. Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing. Nat. Commun. 13, 2074 (2022).
Google Scholar
Dutta, M., Brivio, S. & Spiga, S. Unraveling the roles of switching and relaxation times in volatile electrochemical memristors to mimic neuromorphic dynamical features. Adv. Electron. Mater. 10, 2400221 (2024).
Google Scholar
Yan, X. et al. A low-power reconfigurable memristor for artificial neurons and synapses. Appl. Phys. Lett. 122, 042101 (2023).
Google Scholar
Sahu, M. C. et al. Reconfigurable low-power TiO2 memristor for integration of artificial synapse and nociceptor. ACS Appl. Mater. Interfaces. 15, 25713–25725 (2023).
Google Scholar
Wang, T. et al. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat. Commun. 13, 7432 (2022).
Google Scholar
Li, J. et al. Electrochemical and thermodynamic processes of metal nanoclusters enabled biorealistic synapses and leaky-integrate-and-fire neurons. Mater. Horiz. 7, 71–81 (2020).
Google Scholar
Bao, L. et al. Dual-gated MoS2 neuristor for neuromorphic computing. ACS Appl. Mater. Interfaces. 11, 41482–41489 (2019).
Google Scholar
Li, Y. et al. Memristive field-programmable analog arrays for analog computing. Adv. Mater. 35, 2206648 (2023).
Google Scholar
Passerini, E. et al. Controlling volatility and nonvolatility of memristive devices by Sn alloying. ACS Appl. Electron. Mater. 5, 6842–6849 (2023).
Google Scholar
Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).
Google Scholar
Hsiung, C. P. et al. Formation and instability of silver nanofilament in Ag-based programmable metallization cells. ACS Nano. 4, 5414–5420 (2010).
Google Scholar
Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014).
Google Scholar
Yeon, H. et al. Alloying conducting channels for reliable neuromorphic computing. Nat. Nanotechnol. 15, 574–579 (2020).
Google Scholar
Lee, Y., Mahata, C., Kang, M. & Kim, S. Short-term and long-term synaptic plasticity in Ag/HfO2/SiO2/Si stack by controlling conducting filament strength. Appl. Surf. Sci. 565, 150563 (2021).
Google Scholar
Wang, Z. et al. Threshold switching of ag or Cu in dielectrics: materials, mechanism, and applications. Adv. Funct. Mater. 28, 1704862 (2018).
Ali, A. et al. Versatile GeS-based CBRAM with compliance-current-controlled threshold and bipolar resistive switching for electronic synapses. Appl. Mater. Today. 29, 101554 (2022).
La Barbera, S., Vuillaume, D. & Alibart, F. Filamentary switching: synaptic plasticity through device volatility. ACS Nano. 9, 941–949 (2015).
Google Scholar
Chekol, S. A., Menzel, S., Waser, R. & Hoffmann-Eifert, S. Strategies to control the relaxation kinetics of Ag-based diffusive memristors and implications for device operation. Adv. Electron. Mater. 8, 2200549 (2022).
Google Scholar
Kim, M. K. & Lee, J. S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano. 12, 1680–1687 (2018).
Google Scholar
Chekol, S. A., Nacke, R., Aussen, S. & Hoffmann-Eifert, S. SET kinetics of Ag/HfO2-Based diffusive memristors under various counter-electrode materials. Micromachines 14, 571 (2023).
Google Scholar
Chekol, S. A., Menzel, S., Ahmad, R. W., Waser, R. & Hoffmann-Eifert, S. Effect of the threshold kinetics on the filament relaxation behavior of Ag-based diffusive memristors. Adv. Funct. Mater. 32, 2111242 (2022).
Google Scholar
Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. Neuronal Dynamics: from Single Neurons To Networks and Models of Cognition (Cambridge University Press, 2014).
Pedone, A., Bertani, M., Brugnoli, L. & Pallini, A. Interatomic potentials for oxide glasses: past, present, and future. J. Non-Cryst Solids X. 15, 100115 (2022).
Google Scholar
Thompson, A. P. et al. LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).
Google Scholar
Evans, D. J. & Morriss, G. P. Nonlinear-response theory for steady planar couette flow. Phys. Rev. A. 30, 1528–1530 (1984).
Google Scholar