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  1. Home
  3. Machine Learning To Optimize Additive Manufacturing For Visible Photonics.
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  3. Machine Learning To Optimize Additive Manufacturing For Visible Photonics.

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Machine learning to optimize additive manufacturing for visible photonics.

Andrew Lininger1, Akeshi Aththanayake1, Jonathan Boyd1

  • 1Department of Physics, Case Western Reserve University, 2076 Adelbert Rd., Cleveland, OH 44106, USA.

Nanophotonics (Berlin, Germany)
|December 5, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Physics-informed machine learning enhances additive manufacturing for nanophotonics by integrating physical laws into design. This approach overcomes limitations in current simulation methods, leading to optimized optical devices.

Keywords:
additive manufacturingmachine learningnanophotonicsphysics-informed machine learningtwo-photon polymerization

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Area of Science:

  • Optics and photonics
  • Materials science
  • Computational physics

Background:

  • Additive manufacturing (AM) is crucial for creating advanced nanophotonic devices.
  • Current simulation and optimization methods lack essential physics integration, hindering AM adoption.
  • This leads to suboptimal optical performance in fabricated nanophotonic systems.

Purpose of the Study:

  • To address the limitations in current simulation and optimization methods for AM in nanophotonics.
  • To propose physics-informed design and optimization as a solution for enhanced device performance.
  • To highlight the potential of physics-informed machine learning (PIML) in this domain.

Main Methods:

  • Developing physics-informed design frameworks for nanophotonics.
  • Integrating known physical principles and constraints into the design process.
  • Utilizing machine learning algorithms informed by physical laws (PIML).

    • Main Results:

    • PIML methods can incorporate essential physics directly into the design framework.
    • This approach overcomes barriers caused by the lack of physics-aware simulations.
    • It enables the creation of nanophotonic devices with improved optical responses.

    Conclusions:

    • Physics-informed design and optimization, especially PIML, are well-suited for advanced nanophotonics fabrication.
    • Integrating physics into the design process is key to overcoming current limitations.
    • This methodology promises to enhance the performance and adoption of additive manufacturing in nanophotonics.