Physics-Informed Graph Neural Network for Inverse Design of Integrated Photonic Biosensors

TL;DR

This paper introduces a physics-informed graph neural network that efficiently designs microring resonator biosensors by embedding physical constraints, significantly reducing computational costs while ensuring accurate, physically consistent device geometries.

Contribution

The paper presents a novel physics-informed GNN model that integrates electromagnetic principles into inverse design of photonic biosensors, improving efficiency and physical accuracy.

Findings

Reduces computational cost of biosensor design.

Maintains physical consistency in predicted geometries.

Achieves accurate spectral target matching.

Abstract

Integrated photonic biosensors provide compact, highly sensitive, and label-free platforms for biochemical detection, making them attractive for on-chip and real-time sensing applications. However, their design remains challenging due to complex resonance behaviour, strong coupling effects, and the computational cost associated with repeated full-wave electromagnetic simulations. In particular, inverse design of microring resonator-based sensors requires accurate modelling of geometry-spectrum relationships while satisfying physical constraints such as resonance conditions and spectral sensitivity requirements. In this work, we propose a physics-informed graph neural network (PI-GNN) for the inverse design of a microring resonator biosensor operating in the 1550 nm band. By representing the photonic structure as a graph and embedding resonance-based physical constraints directly into the learning objective, the model captures both structural connectivity and underlying electromagnetic principles. The proposed approach enables efficient prediction of device geometries that achieve target spectral characteristics, reducing reliance on costly simulations while maintaining physical consistency and competitive design accuracy.