Deep learning for inverse design of photonic devices
Abstract/Contents
- Abstract
- Inverse design of photonic devices is to use optimization algorithms to discover optical structures for desired functional characteristics. However, most of these inverse design problems are non-convex in a very high-dimensional space. This thesis will discuss the use of deep learning as an efficient tool for the inverse design of photonic devices. First, I apply Generative Adversarial Networks (GANs) to 3D metagrating design to augment high-performing device patterns. Next, I introduce Global Optimization Networks (GLOnets) which replace the discriminator in GANs with an electromagnetic solver and then train the generator directly from the solver by backpropagating gradients calculated by the adjoint variable method. Then we apply GLOnets to optical multi-layer thin-film stack design. Next, we analyze the mathematical principles behind GLOnets and explain their advantages. Finally, I discuss the application of neural networks as surrogate simulators to speed up simulations in the inverse design. Overall, we envision that combing the modeling capability of deep neural networks and existing physics knowledge could transform the way photonic systems are simulated and designed.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Jiang, Jiaqi, (Researcher of photonic devices) |
|---|---|
| Degree supervisor | Fan, Jonathan Albert |
| Thesis advisor | Fan, Jonathan Albert |
| Thesis advisor | Wetzstein, Gordon |
| Thesis advisor | Zou, James |
| Degree committee member | Wetzstein, Gordon |
| Degree committee member | Zou, James |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jiaqi Jiang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/qv500br5170 |
Access conditions
- Copyright
- © 2022 by Jiaqi Jiang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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