Capacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's Equations

TL;DR

This paper presents a physics-informed neural network (PINN) model that efficiently simulates capacitive touch sensors by embedding Maxwell's equations into the learning process, enabling rapid design and optimization without extensive simulations.

Contribution

The paper introduces a novel PINN-based surrogate model that directly incorporates Maxwell's equations for capacitive sensors, significantly reducing computational time during design.

Findings

PINN accurately predicts sensor behavior in unseen cases.

Model inference is achieved in seconds, much faster than traditional simulations.

Demonstrates potential for accelerated sensor design and optimization.

Abstract

Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. Ensuring robust functionality and stability of the sensors in dynamic environments necessitates profound domain expertise and computationally intensive multi-physics simulations. This paper introduces a novel approach using a Physics-Informed Neural Network (PINN) based surrogate model to accelerate the design process. The PINN model solves the governing electrostatic equations describing the interaction between a finger and a capacitive sensor. Inputs include spatial coordinates from a 3D domain encompassing the finger, sensor, and PCB, along with finger distances. By incorporating the electrostatic equations directly into the neural network's loss function, the model captures the underlying physics. The learned model thus serves as a surrogate sensor model on which inference can be carried out in seconds for different experimental setups without the need to run simulations. Efficacy results evaluated on unseen test cases demonstrate the significant potential of PINNs in accelerating the development and design optimization of capacitive touch sensors.