Efficient, inverse large-scale optimization of diffractive lenses

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 110 × 110 × 46 μm 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 application opportunities for optimizing high-performance microscopy, photolithography, and optical trapping systems using modest computational resources.