Algorithm-Driven Paradigms for Freeform Optical Engineering

Abstract

Advances in modern manufacturing have enabled the multiscalar patterning of dielectric media with nearly arbitrary layouts, presenting unique opportunities to revolutionize the design and fabrication pipeline for photonic technologies. In this Perspective, we discuss how algorithms based on classical optimization and deep learning are establishing a new conceptual framework for freeform optical engineering. These tools can specify suitable design parameters for a desired objective, automate the high-speed optimization of freeform devices, and augment manufacturing processes to mitigate challenges set by freeform fabrication. A central feature of many of these algorithms is their utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. We anticipate that these algorithm-driven methods will streamline optical systems design at the physical limits of structured media and become standard academic and industry tools for scientists and engineers.

Snapshot Mueller spectropolarimeter imager

“…As a metrology tool, incorporating a goniometer stage with our system can enable measurements at different light incidence angles, producing more data for more accurate analysis. Longer term, we envision that our concept can extend to the analysis of microscopic domains through the use of microscope objectives in the 4 f system and that it can be implemented in other imaging and optical data processing modalities through the utilization of metasurface apertures with more customized optical responses 36 – 41 . With proper co-design of aperture responses with software, our imaging system can be tailored for tasks as diverse as optical computing and data compression, and it can combine with concepts in computational imaging to enable enhanced imaging capabilities 42 .…”

Reparameterization Approach to Gradient-Based Inverse Design of Three-Dimensional Nanophotonic Devices

“…We also anticipate that the specification of metasurface layouts with a reparameterization scheme, which produces a significant dimensionality reduction in the metasurface design space, will also enable other complementary algorithms to more effectively operate. These algorithms include high speed surrogate electromagnetic solvers, such as WaveY-Nets, which can dramatically accelerate the local and global − gradient-based optimization of metasurface devices. They also include deep networks that can correct for systematic fabrication errors such as proximity error from device patterning and etching.…”

Neural Operator-Based Surrogate Solver for Free-Form Electromagnetic Inverse Design

“…However, even the most data-efficient models can not offset the cost of data generation if the problem at hand requires only a handful of simulations in the first place. Therefore, we identify inverse design, − specifically gradient-based, as a discipline that is well-suited to benefit from the speed of surrogate models and suffers little from their drawbacks. , In gradient-based inverse design, a functional element is optimized by incrementally maximizing some figure of merit. Gradients of this figure of merit with respect to incremental changes in the geometry are then used to refine the device until an optimum is found iteratively.…”