Toward Memristor-like Resonant Sensors: Observation of Pinched Hysteresis within MEMS Resonators

TL;DR

This paper reports the first observation of memristor-like pinched hysteresis in MEMS resonators, opening new possibilities for integrating MEMS sensors with AI for in-sensor computing and multi-domain applications.

Contribution

It demonstrates that MEMS resonators can exhibit memristor-like hysteresis behavior, enabling the development of novel physical sensors with AI integration capabilities.

Findings

First observation of pinched hysteresis in MEMS resonators

Potential for creating in-physical-sensor AI computing systems

Foundation for MEMS-AI integrated sensor technologies

Abstract

Memristors, uniquely characterized by their pinched hysteresis loop fingerprints, have attracted significant research interest over the past decade, due to their enormous potential for novel computation and artificial intelligence applications. Memristors are widely regarded as the fourth fundamental electrical component, with voltage and current being their input and output signals. In broader terms, similar pinched hysteresis behavior should also exist in other physical systems across domains (e.g., physical input and electrical output), hence linking the real physical world with the digital domain (e.g., in the form of a physical sensor). In this work, we report the first observation of pinched hysteresis behavior in a micro-electro-mechanical systems (MEMS) resonator device, showing that it is viable to create resonant MEMS sensors incorporating memristor-like properties, i.e., MemReSensor. We envisage that this will lay the foundations for a new way of fusing MEMS with artificial intelligence (AI), such as creating in-physical-sensor computing, as well as in-sensor AI, e.g., multi-mode in-sensor matrix multiplication across domains.