Interest in nanophotonics continues to grow as integrated optics provides an affordable platform for areas like telecommunications, quantum information processing, and biosensing. Designing and characterizing integrated photonics components and circuits, however, remains a major bottleneck. This is especially true when complex circuits or devices are required to study a particular phenomenon.To address this challenge, this work develops and experimentally validates a novel machine learning design framework for nanophotonic devices that is both practical and intuitive. As case studies, artificial neural networks are trained to model strip waveguides, integrated chirped Bragg gratings, and microring resonators using a small number of simple input and output parameters relevant to designers. Once trained, the models significantly decrease the computational cost relative to traditional design methodologies. To illustrate the power of the new design paradigm, both forward and inverse design tools enabled by the new design paradigm are demonstrated. These tools are directly used to design and fabricate several integrated Bragg grating devices and ring resonator filters. The method's predictions match the experimental measurements well and do not require any post-fabrication training adjustments.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-8131 |
| Date | 01 April 2019 |
| Creators | Hammond, Alec Michael |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | http://lib.byu.edu/about/copyright/ |
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