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13 March 2023 Deep learning discovery of silicon photonic components
Alagappan Gandhi, Ching Eng Png, Thomas Ang
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Proceedings Volume PC12426, Silicon Photonics XVIII; PC1242605 (2023) https://doi.org/10.1117/12.2651507
Event: SPIE OPTO, 2023, San Francisco, California, United States
Abstract
We establish a systematic framework of photonic device discovery using a physics-based deep learning approach. The computationally expensive physics simulations are removed from the critical loop to generate data and perform one-time training of the deep learning models. Consequently, the trained deep learning models achieve massive speed up on the iterative design process. Our approach reduces the computational time from days to minutes. Using a silicon power divider as an example, we demonstrate discovery of a spectrum of devices that simultaneously satisfy compact footprints, ultralow losses, ultrawide bandwidth, and exceptional robustness against fabrication randomness.
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Alagappan Gandhi, Ching Eng Png, and Thomas Ang "Deep learning discovery of silicon photonic components", Proc. SPIE PC12426, Silicon Photonics XVIII, PC1242605 (13 March 2023); https://doi.org/10.1117/12.2651507
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KEYWORDS
Silicon photonics
Data modeling
Feedback signals
Integrated optics
Physics
Photonic integrated circuits
Quantum optics
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