We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Machine learning to optimize additive manufacturing for visible photonics.
- Authors
Lininger, Andrew; Aththanayake, Akeshi; Boyd, Jonathan; Ali, Omar; Goel, Madhav; Jizhe, Yangheng; Hinczewski, Michael; Strangi, Giuseppe
- Abstract
Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physics of the optimization process leads to barriers for greater adoption. This issue can often result in sub-optimal optical responses in fabricated devices on both local and global scales. We propose that physics-informed design and optimization methods, and in particular physics-informed machine learning, are particularly well-suited to overcome these challenges by incorporating known physics, constraints, and fabrication knowledge directly into the design framework.
- Subjects
MACHINE learning; NANOPHOTONICS; ACCOUNTING methods; FEED additives
- Publication
Nanophotonics (21928606), 2023, Vol 12, Issue 14, p2767
- ISSN
2192-8606
- Publication type
Article
- DOI
10.1515/nanoph-2022-0815