Publication date: 24th July 2026
Ion-motion-mediated memristors provide a unique physical platform in which the same underlying ionic dynamics can be engineered toward two seemingly opposite goals: deterministic, repeatable conductance modulation for neuromorphic computing, and stochastic, intrinsically unpredictable switching for probabilistic computing and security-oriented applications. This talk surveys emerging computing paradigms enabled by ion motion and then delineates deterministic versus stochastic switching as two regimes defined by the controllability of ion motion and filamentary evolution.
First, we present an ion-motion control strategy for reliable nonvolatile memristors using a Ru active electrode and a SiO2 nanorod matrix. By reducing the activation barrier for Ru ion diffusion and stabilizing redox reactions, precise tuning of Ru ion dynamics yields electroforming-free operation, low-power switching, highly linear conductance modulation, and inherent on-state nonlinearity. Mechanistically, the nanorod architecture promotes a sparse nanocluster-based conduction path, shifting transport toward Fowler-Nordheim tunneling and thereby suppressing sneak currents. We further discuss crossbar-level implications, including selector-less scalability and a 16×16 array demonstration with multi-bit uniformity, supported by an integrated spiking neural network simulator that accounts for line resistance and sneak-current effects.
Second, we shift to ion-motion-mediated volatile threshold-switching memristors as physical entropy sources. Using a porous nanorod oxide layer to enhance cation pathways and tuning the effective Ag-ion supply, we obtain rapid, device-centric digital and analog random outputs without post-processing. To uncover the origin of stochasticity, we directly visualize multi-filament dynamics via scanning thermal microscopy and corroborate the coupled electrothermal mechanism with simulations, clarifying how electromigration, Joule heating, and surface-energy-driven filament instability shape switching statistics. Finally, we highlight system-level implementations, including a bimodal true random number generator and a probabilistic computing platform (p-bit) enabled by controllable stochastic behaviors.
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) and the Korea Basic Science Institute (KBSI), funded by the Ministry of Science and ICT (RS-2024-00406418, RS-2024-00403917, RS-2025-02215065, and RS-2026-25480979)
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