Efficient, inverse large-scale optimization of diffractive lenses

TL;DR

This paper introduces an efficient inverse large-scale optimization method for diffractive lenses, significantly improving their performance while reducing computational costs through a novel integration of the convergent Born series with adjoint-field optimization.

Contribution

The authors develop a scalable optimization framework that enables large-domain inverse design of diffractive lenses using minimal computational resources.

Findings

Achieved 9% better axial resolution compared to standard lenses.

Realized 20% higher focusing efficiency with the optimized lens.

Enabled large-scale inverse design on a single graphics card.

Abstract

Scalable photonic optimization holds the promise of significantly enhancing the performance of diffractive lenses across a wide range of photonic applications. However, the high computational cost of conventional full three-dimensional electromagnetic solvers has thus far been a major obstacle to large-scale-domain optimization. Here, we address this limitation by integrating the convergent Born series with the adjoint-field optimization framework, enabling inverse design with its domain size up to a $110 \times 110 \times 46\ \mu\text{m}^3$ volume$-$corresponding to 0.1 gigavoxels$-$using a single, cost-effective graphics card. The optimized lens achieves a 9% improvement in axial resolution and a 20% increase in focusing efficiency compared to a standard Fresnel lens of identical diameter and numerical aperture. These gains point to immediate application opportunities for optimizing high-performance microscopy, photolithography, and optical trapping systems using modest computational resources.