Details
| Original language | English |
|---|---|
| Title of host publication | Photonic and Phononic Properties of Engineered Nanostructures XV |
| Editors | Ali Adibi, Shawn-Yu Lin, Axel Scherer |
| Publisher | SPIE |
| ISBN (electronic) | 9781510685024 |
| Publication status | Published - 19 Mar 2025 |
| Event | Photonic and Phononic Properties of Engineered Nanostructures XV - San Francisco, United States Duration: 27 Jan 2025 → 31 Jan 2025 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 13377 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leveraging a flexible reverse-mode automatic differentiation implementation, our software enables gradient-based optimization over large simulation volumes. Gradient computation is implemented within the JAX framework and based on the property of time reversibility in Maxwell’s equations. This approach significantly reduces computational time and memory requirements compared to traditional FDTD methods. Gradient-based optimization facilitates the automatic creation of intricate three-dimensional structures with millions of design parameters, which would be infeasible to design manually. We demonstrate the scalability of the solver from single to multiple GPUs through several inverse design examples, highlighting its robustness and performance in large-scale photonic simulations. In addition, the package features an object-oriented and user-friendly API that simplifies the specification of materials, sources, and constraints. Specifically, it allows for intuitive positioning and sizing of objects in absolute or relative coordinates within the simulation scene. By rapid specification of the desired design properties and rapid optimization within the given user constraints, this open-source framework aims to accelerate innovation in photonic inverse design. It yields a powerful and accessible computational tool for researchers, applicable in a wide range of use cases, including but not limited to photonic waveguides, active devices, and photonic integrated circuits.
Keywords
- Automatic Differentiation, FDTD, Inverse Design
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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- Apa
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- BibTeX
- RIS
Photonic and Phononic Properties of Engineered Nanostructures XV. ed. / Ali Adibi; Shawn-Yu Lin; Axel Scherer. SPIE, 2025. 1337709 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 13377).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - A flexible framework for large-scale FDTD simulations
T2 - Photonic and Phononic Properties of Engineered Nanostructures XV
AU - Mahlau, Yannik
AU - Schubert, Frederik
AU - Bethmann, Konrad
AU - Caspary, Reinhard
AU - Lesina, Antonio Calà
AU - Munderloh, Marco
AU - Ostermann, Jörn
AU - Rosenhahn, Bodo
N1 - Publisher Copyright: © 2025 SPIE.
PY - 2025/3/19
Y1 - 2025/3/19
N2 - We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leveraging a flexible reverse-mode automatic differentiation implementation, our software enables gradient-based optimization over large simulation volumes. Gradient computation is implemented within the JAX framework and based on the property of time reversibility in Maxwell’s equations. This approach significantly reduces computational time and memory requirements compared to traditional FDTD methods. Gradient-based optimization facilitates the automatic creation of intricate three-dimensional structures with millions of design parameters, which would be infeasible to design manually. We demonstrate the scalability of the solver from single to multiple GPUs through several inverse design examples, highlighting its robustness and performance in large-scale photonic simulations. In addition, the package features an object-oriented and user-friendly API that simplifies the specification of materials, sources, and constraints. Specifically, it allows for intuitive positioning and sizing of objects in absolute or relative coordinates within the simulation scene. By rapid specification of the desired design properties and rapid optimization within the given user constraints, this open-source framework aims to accelerate innovation in photonic inverse design. It yields a powerful and accessible computational tool for researchers, applicable in a wide range of use cases, including but not limited to photonic waveguides, active devices, and photonic integrated circuits.
AB - We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leveraging a flexible reverse-mode automatic differentiation implementation, our software enables gradient-based optimization over large simulation volumes. Gradient computation is implemented within the JAX framework and based on the property of time reversibility in Maxwell’s equations. This approach significantly reduces computational time and memory requirements compared to traditional FDTD methods. Gradient-based optimization facilitates the automatic creation of intricate three-dimensional structures with millions of design parameters, which would be infeasible to design manually. We demonstrate the scalability of the solver from single to multiple GPUs through several inverse design examples, highlighting its robustness and performance in large-scale photonic simulations. In addition, the package features an object-oriented and user-friendly API that simplifies the specification of materials, sources, and constraints. Specifically, it allows for intuitive positioning and sizing of objects in absolute or relative coordinates within the simulation scene. By rapid specification of the desired design properties and rapid optimization within the given user constraints, this open-source framework aims to accelerate innovation in photonic inverse design. It yields a powerful and accessible computational tool for researchers, applicable in a wide range of use cases, including but not limited to photonic waveguides, active devices, and photonic integrated circuits.
KW - Automatic Differentiation
KW - FDTD
KW - Inverse Design
UR - http://www.scopus.com/inward/record.url?scp=105002727688&partnerID=8YFLogxK
U2 - 10.1117/12.3052639
DO - 10.1117/12.3052639
M3 - Conference contribution
AN - SCOPUS:105002727688
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Photonic and Phononic Properties of Engineered Nanostructures XV
A2 - Adibi, Ali
A2 - Lin, Shawn-Yu
A2 - Scherer, Axel
PB - SPIE
Y2 - 27 January 2025 through 31 January 2025
ER -