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 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
We present two different approaches to apply deep learning to inverse design for nanophotonic devices. First, we use a regression model, with device parameters as inputs and device responses as outputs, or vice versa. Second, we use a novel generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios.