Photonic Inverse Design Through Machine Learning and Correlated Surrogate Annealing
We present a machine-learning framework for nano-device inverse design. Using correlated surrogate annealing, our framework generates designs orders of magnitude faster than traditional generative models, offering broad applicability to general inverse design problems.
Authors
- Blake A. Wilson (ORCID: https://orcid.org/0000-0002-3055-8334)
- Sabre Kais (ORCID: https://orcid.org/0000-0003-0574-5346)
- Alexander V. Kildishev (ORCID: https://orcid.org/0000-0002-8382-8422)
- Vaishnavi Iyer
- Brad Lackey (ORCID: https://orcid.org/0000-0002-3823-8757)
- Michael Bezick
- Yuheng Chen (ORCID: https://orcid.org/0009-0008-2896-6831)
- Vladimir M. Shalaev
- Alexandra Boltasseva
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_fs.2025.ff129_3
- Primary Topic
- Advanced Optical Imaging Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00