Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects
Nanophotonics, which explores significant light–matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photo...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-01-01
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| Series: | Nanophotonics |
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| Online Access: | https://doi.org/10.1515/nanoph-2024-0536 |
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| author | Kim Junhyeong Kim Jae-Yong Kim Jungmin Hyeong Yun Neseli Berkay You Jong-Bum Shim Joonsup Shin Jonghwa Park Hyo-Hoon Kurt Hamza |
| author_facet | Kim Junhyeong Kim Jae-Yong Kim Jungmin Hyeong Yun Neseli Berkay You Jong-Bum Shim Joonsup Shin Jonghwa Park Hyo-Hoon Kurt Hamza |
| author_sort | Kim Junhyeong |
| collection | DOAJ |
| description | Nanophotonics, which explores significant light–matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field. |
| format | Article |
| id | doaj-art-5f522ab2f6aa49d18f289727d467a588 |
| institution | Kabale University |
| issn | 2192-8614 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj-art-5f522ab2f6aa49d18f289727d467a5882025-02-10T13:24:47ZengDe GruyterNanophotonics2192-86142025-01-0114212115110.1515/nanoph-2024-0536Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospectsKim Junhyeong0Kim Jae-Yong1Kim Jungmin2Hyeong Yun3Neseli Berkay4You Jong-Bum5Shim Joonsup6Shin Jonghwa7Park Hyo-Hoon8Kurt Hamza9The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaThe School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI53706, USAThe School of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaThe School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaNational Nanofab Center (NNFC), Daejeon, Republic of KoreaThe School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaThe School of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaThe School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaThe School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaNanophotonics, which explores significant light–matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field.https://doi.org/10.1515/nanoph-2024-0536nanophotonicssilicon photonicsinverse designoptimizationartificial intelligencedeep learning |
| spellingShingle | Kim Junhyeong Kim Jae-Yong Kim Jungmin Hyeong Yun Neseli Berkay You Jong-Bum Shim Joonsup Shin Jonghwa Park Hyo-Hoon Kurt Hamza Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects Nanophotonics nanophotonics silicon photonics inverse design optimization artificial intelligence deep learning |
| title | Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects |
| title_full | Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects |
| title_fullStr | Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects |
| title_full_unstemmed | Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects |
| title_short | Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects |
| title_sort | inverse design of nanophotonic devices enabled by optimization algorithms and deep learning recent achievements and future prospects |
| topic | nanophotonics silicon photonics inverse design optimization artificial intelligence deep learning |
| url | https://doi.org/10.1515/nanoph-2024-0536 |
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