Deep-learning enabled design of the flow of light in complex nanophotonic devices

Otto L. Muskens; Tom Radford; Nicholas Dinsdale; Peter Wiecha; Alberto Politi

doi:10.1117/12.2658891

17 March 2023 Deep-learning enabled design of the flow of light in complex nanophotonic devices

Otto L. Muskens, Tom Radford, Nicholas Dinsdale, Peter Wiecha, Alberto Politi

Proceedings Volume PC12425, Smart Photonic and Optoelectronic Integrated Circuits 2023; PC124250D (2023) https://doi.org/10.1117/12.2658891
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

This presentation discusses new ways of designing nanophotonic devices using deep learning. I will present our efforts in developing generalized artificial neural network (ANN) approaches for evaluating 3D nanostructures in free space and integrated photonic circuits, taking into account complex interactions. New results on highly multi-objective, ANN-driven inverse design of complex scattering matrices in multimode silicon photonic waveguides is presented, which enable ultracompact routers and programmable switches for photonic AI and quantum chips.

Conference Presentation

Citation Download Citation

Otto L. Muskens, Tom Radford, Nicholas Dinsdale, Peter Wiecha, and Alberto Politi "Deep-learning enabled design of the flow of light in complex nanophotonic devices", Proc. SPIE PC12425, Smart Photonic and Optoelectronic Integrated Circuits 2023, PC124250D (17 March 2023); https://doi.org/10.1117/12.2658891

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