Gradient-Based Optimization of MEMS Loudspeaker Equivalent Circuit Models via Automatic Differentiation

Micro-Electro-Mechanical Systems (MEMS) loudspeakers represent a promising solution to meet the growing demand for compact, portable consumer audio devices with integrated sound reproduction capabilities. In this context, the availability of accurate and computationally efficient Lumped-Element Models (LEMs) can greatly accelerate MEMS loudspeaker design and support the development of digital signal processing techniques aimed at enhancing audio performance. In this work, we propose a framework based on Automatic Differentiation (AD) to optimize the parameters of differentiable LEMs in a fully data-driven manner using standard gradient-based optimization methods. Specifically, we focus on tuning the parameters of an ad hoc linear equivalent circuit model for a commercially available MEMS loudspeaker intended for free-field applications.