Memristors offer a promising solution to address the performance and energy challenges faced by conventional von Neumann computer systems. Yet, stochastic ion migration in conductive filament often leads to an undesired performance tradeoff between memory window, retention, and endurance. Herein, a robust memristor based on oxygen-rich SnO2 nanoflowers switching medium, enabled by seed-mediated wet chemistry, to overcome the ion migration issue for enhanced analog in-memory computing is reported. Notably, the interplay between the oxygen vacancy (Vo) and Ag ions (Ag+ ) in the Ag/SnO2 /p++ -Si memristor can efficiently modulate the formation and abruption of conductive filaments, thereby resulting in a high on/off ratio (>106), long memory retention (10-year extrapolation), and low switching variability (SV = 6.85%). Multiple synaptic functions, such as paired-pulse facilitation, long-term potentiation/depression, and spike-time dependent plasticity, are demonstrated. Finally, facilitated by the symmetric analog weight updating and multiple conductance states, a high image recognition accuracy of ≥ 91.39% is achieved, substantiating its feasibility for analog in-memory computing. This study highlights the significance of synergistically modulating conductive filaments in optimizing performance trade-offs, balancing memory window, retention, and endurance, which demonstrates techniques for regulating ion migration, rendering them a promising approach for enabling cutting-edge neuromorphic applications.
Keywords: in-memory computing; ion migration; memristor; oxygen vacancies; tunable filaments.
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