A newcomer's guide to deep learning for inverse design in nano-photonics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917581698367488 |
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| author | Khaireh-Walieh, Abdourahman Langevin, Denis Bennet, Pauline Teytaud, Olivier Moreau, Antoine Wiecha, Peter R. |
| author_facet | Khaireh-Walieh, Abdourahman Langevin, Denis Bennet, Pauline Teytaud, Olivier Moreau, Antoine Wiecha, Peter R. |
| contents | Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08618 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A newcomer's guide to deep learning for inverse design in nano-photonics Khaireh-Walieh, Abdourahman Langevin, Denis Bennet, Pauline Teytaud, Olivier Moreau, Antoine Wiecha, Peter R. Optics Computational Physics Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits. |
| title | A newcomer's guide to deep learning for inverse design in nano-photonics |
| topic | Optics Computational Physics |
| url | https://arxiv.org/abs/2307.08618 |