TR2020-130
Inverse Design of Nanophotonic Devices using Deep Neural Networks
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- , "Inverse Design of Nanophotonic Devices using Deep Neural Networks", Asia Communications and Photonics Conference (ACP), September 2020, pp. Su1A.1.BibTeX TR2020-130 PDF Video
- @inproceedings{Kojima2020sep,
- author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh K. and Parsons, Kieran and TaherSima, Mohammad and Sang, Fengqiao and Klamkin, Jonathan and Qi, Minghao},
- title = {{Inverse Design of Nanophotonic Devices using Deep Neural Networks}},
- booktitle = {Asia Communications and Photonics Conference (ACP)},
- year = 2020,
- pages = {Su1A.1},
- month = sep,
- publisher = {Optical Society of America},
- isbn = {978-1-943580-82-8},
- url = {https://www.merl.com/publications/TR2020-130}
- }
- , "Inverse Design of Nanophotonic Devices using Deep Neural Networks", Asia Communications and Photonics Conference (ACP), September 2020, pp. Su1A.1.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Electronic and Photonic Devices, Machine Learning, Optimization, Signal Processing
Abstract:
We present three different approaches to apply deep learning to inverse design for nanophotonic devices. The forward and inverse regression models use device parameters as inputs and device responses as outputs, and vice versa. The generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios.