TR2023-036
Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design
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- , "Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design", Conference on Lasers and Electro-Optics (CLEO), DOI: 10.1364/CLEO_FS.2023.FW4C.7, May 2023.BibTeX TR2023-036 PDF
- @inproceedings{Koike-Akino2023may,
- author = {Koike-Akino, Toshiaki and Jung, Minwoo and Chakrabarty, Ankush and Wang, Ye and Kojima, Keisuke and Brand, Matthew},
- title = {{Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design}},
- booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
- year = 2023,
- month = may,
- doi = {10.1364/CLEO_FS.2023.FW4C.7},
- url = {https://www.merl.com/publications/TR2023-036}
- }
- , "Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design", Conference on Lasers and Electro-Optics (CLEO), DOI: 10.1364/CLEO_FS.2023.FW4C.7, May 2023.
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MERL Contacts:
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Research Areas:
Communications, Electronic and Photonic Devices, Machine Learning, Optimization, Signal Processing
Abstract:
We propose a new device optimization framework based on Bayesian optimization for efficient latent sampling of adversarial generative neural networks to expedite a complex inverse design of tunable nanophotonic wavelength splitters. Our design, at broadband telecomm-wavelengths, is electrically switchable via liquid crystal tuning.