Microring Resonator Dispersion Metrology with Neural Networks

TL;DR

This paper presents a machine learning framework that accurately predicts microring resonator dimensions, identifies material dispersion, and reconstructs dispersion spectra from geometric data, enabling rapid, non-destructive dispersion metrology for photonic fabrication.

Contribution

The work introduces a neural network-based approach for inverse and forward dispersion characterization, significantly improving speed and accuracy over traditional methods.

Findings

Achieves sub-1 nm accuracy in ring dimension prediction without noise.

Reaches up to 8 nm accuracy with realistic measurement noise.

Dispersion classification exceeds 99% accuracy.

Abstract

Precise knowledge of resonator dispersion, from both geometric and material contributions, is essential for reliable high-performance nonlinear integrated photonics devices, such as optical parametric oscillators, frequency doublers, and integrated optical frequency combs. However, direct measurements at the fabrication level provide limited knowledge, whether through destructive cross-section imaging or non-destructive ellipsometry, while complete optical characterization that enables precise dispersion metrology is time-consuming and poorly suited for mass-scale foundry fabrication. In this work, we develop a machine learning framework to solve three complementary problems: (i) predicting resonator geometric dimensions, (ii) identifying the correct material dispersion, and last, but not least, (iii) precisely reconstructing the integrated dispersion spectrum directly from ring dimensions. These three neural networks together enable both inverse and forward characterization of microring resonators. Using numerically generated datasets based on Sellmeier-type material models, we demonstrate <1 nm ring dimension prediction accuracy without noise, <8 nm prediction accuracy with ~45 dispersion samples under a realistic frequency measurement noise level (50 MHz), and ~16 nm prediction accuracy at a higher noise level (200 MHz). The Sellmeier model classification exceeds 99% accuracy in all cases. Importantly, dispersion sampled far from the pump resonances proves most informative, reducing full-spectrum characterization requirements. The forward-prediction network reconstructs dispersion spectra from the ring dimensions with high accuracy. Our results highlight the potential of machine learning applied to dispersion data as a rapid, non-destructive tool for wafer-scale quality control and process monitoring in photonic foundries.