Experimental machine learning for aperiodic wafer-scale photonics inverse design

Maksim Makarenko; Arturo Burguete-Lopez; Sergey Rodionov; Qizhou Wang; Fedor Getman; Andrea Fratalocchi

doi:10.1117/12.3017331

18 June 2024 Experimental machine learning for aperiodic wafer-scale photonics inverse design

Maksim Makarenko, Arturo Burguete-Lopez, Sergey Rodionov, Qizhou Wang, Fedor Getman, Andrea Fratalocchi

Proceedings Volume 13017, Machine Learning in Photonics; 130170F (2024) https://doi.org/10.1117/12.3017331
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

In this work, we propose a novel framework for large-scale aperiodic nanophotonic inverse design utilizing an experimental machine-learning technique. With this technique, we create an extensive dataset of 10 million experimental structures for enhanced flat-optics design. This largest publicly available inverse design dataset, achieved through electron beam lithography, bypasses the extensive computational demand of first-principle simulations. Experimental acquisition ensures the dataset embodies real-world variances, leading to ML models with a ten-fold improved prediction accuracy in optical responses, drastically reducing validation RMSE from 0.012 to 0.0018. With this dataset, we developed a framework for large-scale aperiodic photonics design capable of designing tens of structures per second. We demonstrate the efficiency of the proposed technique by creating a large (3x3mm) aperiodic photonic structure composed of >10000 individual structures with pre-defined transmission/reflection responses.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Maksim Makarenko, Arturo Burguete-Lopez, Sergey Rodionov, Qizhou Wang, Fedor Getman, and Andrea Fratalocchi "Experimental machine learning for aperiodic wafer-scale photonics inverse design", Proc. SPIE 13017, Machine Learning in Photonics, 130170F (18 June 2024); https://doi.org/10.1117/12.3017331

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