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Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and
Junhyeong Kim1, Jae-Yong Kim1, Jungmin Kim2
1The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Nanophotonics (Berlin, Germany)
|February 10, 2025
Summary
Artificial intelligence and optimization methods are revolutionizing nanophotonics by automating the design of advanced nanophotonic devices. This review explores cutting-edge inverse design techniques for next-generation photonics.
Area of Science:
- Nanophotonics and nanoscale light-matter interactions.
- Development of ultra-compact, high-performance nanophotonic devices.
Background:
- Traditional forward design methods for nanophotonic devices have limitations.
- Advancements in AI and optimization offer new design paradigms.
Purpose of the Study:
- To review the latest progress in inverse design for nanophotonic devices.
- To explore AI and optimization methods for automated nanophotonic design.
Main Methods:
- Discusses meta-heuristic algorithms (evolutionary, swarm-based) and adjoint-based optimization.
- Explores deep learning techniques: discriminative, generative, and reinforcement learning.
- Categorizes inverse-designed nanophotonic devices and their methodologies.
Main Results:
- Highlights the application of AI and optimization in automating nanophotonic device design.
- Presents a categorization of inverse-designed devices and their associated methods.
- Summarizes available open-source tools and commercial foundries for inverse design.
Conclusions:
- Inverse design, powered by AI and optimization, is crucial for next-generation nanophotonics.
- Identifies current challenges and future research directions in the field.
- Emphasizes the potential for further advancements in automated nanophotonic device design.
