TR2022-107
Deep Transfer Learning for Nanophotonic Device Design
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- , "Deep Transfer Learning for Nanophotonic Device Design", Conference on Lasers and Electro-Optics (CLEO) Pacific Rim, July 2022.BibTeX TR2022-107 PDF
- @inproceedings{Kojima2022jul,
- author = {Kojima, Keisuke and Jung, Minwoo and Koike-Akino, Toshiaki and Wang, Ye and Brand, Matthew and Parsons, Kieran},
- title = {{Deep Transfer Learning for Nanophotonic Device Design}},
- booktitle = {Proceedings of the 2022 Conference on Lasers and Electro-Optics Pacific Rim},
- year = 2022,
- month = jul,
- publisher = {Optica Publishing Group},
- url = {https://www.merl.com/publications/TR2022-107}
- }
- , "Deep Transfer Learning for Nanophotonic Device Design", Conference on Lasers and Electro-Optics (CLEO) Pacific Rim, July 2022.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Electronic and Photonic Devices, Machine Learning, Optimization, Signal Processing
Abstract:
Applying a transfer-learning technique for generative deep neural networks, we demonstrate a very time-efficient inverse design framework for photonic integrated circuit devices, when there are new demands for structural/material parameters from an existing device library.