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Physics-model-based neural networks for inverse design of binary phase planar diffractive lenses
Optics Letters
|March 22, 2023
Summary
This study introduces an efficient inverse design method using physics-model-based neural networks (PMNNs) to engineer binary phase planar diffractive lenses (BPPDLs). This approach significantly reduces design time for photonic devices with customized functionalities.
Area of Science:
- Optics and Photonics
- Computational Physics
- Materials Science
Background:
- Inverse design enables customized photonic devices, but traditional optimization algorithms struggle with complex, multi-constrained problems due to long computation times.
- Existing data-driven deep learning methods for inverse design can be limited by the need for extensive training datasets and may not fully leverage physical principles.
Purpose of the Study:
- To develop an efficient inverse design method for engineering the focusing behavior of binary phase planar diffractive lenses (BPPDLs).
- To demonstrate the capability of designing BPPDLs with diverse functionalities, including single/multiple focal spots and diffraction-limit-sized optical needles.
- To significantly reduce the computational time required for designing complex photonic devices.
Main Methods:
- Utilized physics-model-based neural networks (PMNNs) integrated with Rayleigh-Sommerfeld diffraction theory for inverse design.
- Employed the PMNN approach to engineer the functionalities of binary phase planar diffractive lenses (BPPDLs).
- Validated the method by designing devices for single focal spot, multiple foci, and optical needle generation.
Main Results:
- Successfully designed BPPDLs with tailored focusing properties, including single focus, multiple foci, and an optical needle approaching the diffraction limit.
- Demonstrated a dramatic reduction in design time for a single photonic device, down to several minutes.
- Showcased the efficiency of PMNNs over traditional optimization and data-driven deep learning methods for complex inverse design problems.
Conclusions:
- The proposed PMNN-based inverse design method offers an efficient and versatile solution for engineering photonic devices with customized functionalities.
- This approach overcomes the time constraints and limitations of conventional optimization algorithms and traditional data-driven deep learning.
- Provides a powerful tool for accelerating the development of advanced diffractive optical elements and other photonic devices.
