Ion Intercalation Enabled Tunable Frequency Response in Lithium Niobite Memristors

Memristors have emerged as a viable component for developing neuromorphic hardware platforms, which can compete with biological systems in density, accuracy, and energy efficiency. Among contemporary memristive systems, intercalation-driven lithium niobite (LiNbO2) memristors have the advantage of inherent flux-driven large analog conductance tunability (
$\Delta {R}/{R} >89$
), a wide resistance range (~10–1E
$7 \Omega $
) selected via geometry selection, and low-power (~100–150 mV) neuromorphic functionality resembling synaptic weight updates in the synaptic membrane. Emerging neural systems require nonstatic temporal responses to implement temporally diverse architectures, such as recurrent neural networks (RNNs), yet these temporally diverse memristors are rare. There is a gap in understanding of the frequency response of nonvolatile memristors, which is a fundamental characteristic in memristive systems and ultimately dictates the speed of adaptive learning in deployed neural networks and the memory windows available for designers in RNNs. Using both large and small signal characterization methods, these memristors demonstrate up to a decade of span in tunable frequency-dependent response, statically controlled via device geometry and scaling design rules, and dynamically tuned via channel lithium concentration. Thus, the tunable frequency dependence of resistance modulation in LiNbO2 memristors is evaluated, enabling future neural network design rules to be identified for scalable multifunctional memristive systems for neuromorphic computing applications.