Massively parallel pixel-by-pixel nanophotonic optimization using a Green's function formalism

TL;DR

This paper presents a highly parallelized pixel-by-pixel nanophotonic optimization method using Green's functions, enabling large-scale inverse design problems and connecting to reinforcement learning concepts.

Contribution

It introduces a scalable parallelization scheme for Green's function-based nanophotonic optimization, allowing for tackling larger, more complex inverse design problems.

Findings

Successfully optimized a high numerical aperture metalens.

Demonstrated efficient distribution of computational tasks across thousands of workers.

Connected nanophotonic inverse design to reinforcement learning ideas.

Abstract

We introduce an efficient parallelization scheme to implement pixel-by-pixel nanophotonic optimization using a Green's function based formalism. The crucial insight in our proposal is the reframing of the optimization algorithm as a large-scale data processing pipeline, which allows for the efficient distribution of computational tasks across thousands of workers. We demonstrate the utility of our implementation by exercising it to optimize a high numerical aperture focusing metalens at problem sizes that would otherwise be far out of reach for the Green's function based method. Finally, we highlight the connection to powerful ideas from reinforcement learning as a natural corollary of reinterpreting the nanophotonic inverse design problem as a graph traversal enabled by the pixel-by-pixel optimization paradigm.