Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides

Aneek E. James; Alexander Wang; Songli Wang; Keren Bergman

doi:10.1117/12.2594255

1 August 2021 Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides

Aneek E. James, Alexander Wang, Songli Wang, Keren Bergman

Author Affiliations +

Proceedings Volume 11843, Applications of Machine Learning 2021; 1184305 (2021) https://doi.org/10.1117/12.2594255
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

For integrated silicon photonics to mature as an industry platform, robust methods for measuring and extracting the geometry of fabricated waveguides are needed. Due to the cost and time needed for SEM or AFM imaging, a method of extracting waveguide variability though optical measurements is often preferred. Here, we present a study of regression-based machine learning (ML) techniques that enable such variability extraction while maintaining compatibility with wafer-scale optical measurements. We first explicitly investigate the issue of non-unique effective and group index pairs that can affect the accuracy of regression-based techniques. Training data is then generated by simulating several geometries of wire waveguides in Lumerical’s MODE solver to simulate defects due to process variances. Finally, a representative set of ML regression techniques are tested for their ability to accurately estimate the geometries of said simulated waveguides. To the best of the authors' knowledge, this work represents the first attempt in the literature to i.) explicitly study the effects of non-uniqueness in optical measurement-based metrology and ii) present a model that potentially overcomes said non-uniqueness. This work represents an important step towards the maturing models for process variations in silicon photonic platforms.

Conference Presentation

Citation Download Citation

Aneek E. James, Alexander Wang, Songli Wang, and Keren Bergman "Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides", Proc. SPIE 11843, Applications of Machine Learning 2021, 1184305 (1 August 2021); https://doi.org/10.1117/12.2594255

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available