Inverse design of large-scale freeform meta-optics by breaking the memory wall of full-wave simulation
TL;DR
A validated framework that removes a memory wall and makes globally optimized freeform meta-optics practical, and substantially outperform meta-atom designs, reaching 1.64, 1.65 and 1.24 times their objectives.
Abstract
Gradient-based design of large optical surfaces hits a memory wall: a stored-history time-domain adjoint needs $24N_t$ bytes per cell for $N_t$ steps, and every field update is bound by memory bandwidth. Here we present a validated framework that removes this wall and makes globally optimized freeform meta-optics practical. Its finite-difference time-domain adjoint reconstructs interior fields backwards from two recorded planes, so volume memory no longer grows with $N_t$, and fused kernels raise memory-bandwidth use on an H200 GPU from 6.4% to a remarkable 56% of peak. Overlapping tiles, coupled by angular-spectrum propagation and distributed over GPUs, keep tile memory independent of the aperture. Within hours on one GPU node, we optimize, against one global objective, every density variable of single-layer metasurfaces 100 to 200 $\mu$m across, and design a nine-wavelength lens, a color hologram and a polarization-switched hologram. Confirmed by an independent solver within 1.6%, they substantially outperform meta-atom designs, reaching 1.64, 1.65 and 1.24 times their objectives.