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Deep Learning (DL) shows promise for optical inverse-design of nanostructures. This review surveys DL applications, discusses limitations, and suggests future research directions for nanophotonics.

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Artificial Intelligence

Background:

  • Optical inverse-design is crucial for developing novel nanostructures.
  • Deep Learning (DL) has emerged as a powerful tool in this field.
  • Recent advancements show increasing complexity in both nanostructure design and DL methodologies.

Purpose of the Study:

  • To comprehensively review Deep Learning-based nanostructure design examples in the nanophotonics literature.
  • To assess the limitations and range of validity of DL in optical inverse-design.
  • To provide a perspective on future research directions and potential improvements.

Main Methods:

  • Literature survey of Deep Learning applications in optical nanostructure inverse-design.
  • Analysis of the increasing complexity and sophistication of DL methodologies used.
  • Identification and discussion of limitations and challenges associated with DL approaches.

Main Results:

  • Early results demonstrate DL's disruptive potential in optical inverse-design.
  • A steady increase in the complexity of designed nanostructures and DL methods over the past three years.
  • Identification of areas where DL's effectiveness and limitations require further assessment.

Conclusions:

  • Deep Learning is a rapidly advancing technique for nanophotonic inverse-design.
  • Further research is needed to fully understand DL's limitations and its integration with established methods.
  • This review aims to guide researchers in selecting appropriate DL setups and improving existing workflows.