2026 Li Memristive Solid State Nanopores

Nanofluidic memristors whose conductance evolves through history-dependent ionic transport and dynamic interfacial processes are promising building blocks for ionic neuromorphic applications. However, most existing designs rely on biological nanopores, polymers, and two-dimensional materials, which limit scalable fabrication and pose challenges to integration of ionic computing circuits and systems. Here, we report memristive behaviors of silicon-based solid-state nanopores (SSNPs) fabricated based on wafer-scale semiconductor processes. The SSNPs exhibit hysteretic current-voltage characteristics with a dependence on voltage sweeping frequency, electrolyte concentration, and nanopore geometry. To investigate the physical origin of their memory feature, the measured current of the SSNPs is decomposed into resistive, capacitive, and memristive components. An ion adsorption-desorption kinetics is developed to explain and predict the memristive behavior. A dynamical system analysis further reveals that the memristive behavior arises from delayed relaxation, thereby linking the measured hysteresis to the observed adaptive ionic response. Together, these findings establish native SSNPs as scalable ionic memristive elements and provide a generalized electrokinetic mechanism for memristive behavior under nanoconfinement. The resulting analytical framework connects device characterization with the underlying dynamics, deepens mechanism understanding, and guides the design of ionic neuromorphic devices.