Exploring the Tunability of a Memristor-Like Device Based on MEMS Resonators

Memristors have been increasingly explored as a potential technological platform for novel computation approaches, including artificial intelligence (AI) applications. There are several properties that are referred to in the literature to define a memristor, one of which being the pinched hysteresis property. In our recent research we show that cross-domain (i.e. physical input, electrical output) pinched hysteresis and multiple pinched hysteresis (MPH) systems can be created using a generic, silicon-based micro-electro-mechanical systems (MEMS) resonator device. Despite demonstrating the feasibility of our approach, which is ideally placed to be exploited for in-physical-sensor computing and in-sensor AI, there are challenges facing the successful implementation of such a sensor, for instance, the need to dynamically tune the behavior for a given application and desired performance metrics. In this work, for the first time, we demonstrate a high degree of tunability of the pinched hysteresis behavior using a generic resonant MEMS device by adjusting the parametric modulation (PM) signal parameters used to generate the virtual (dynamic) coupling between the modes.[2025-0199]