Abstract

This talk is focused on HiLAB (Hybrid inverse-design with Latent-space learning, Adjoint-based partial optimizations, and Bayesian optimization) as a new paradigm for inverse design of large-scale freeform nanophotonic devices, e.g., metamaterials. HiLAB integrates early-terminated topological optimization combined with image augmentation to generate a reliable training set for machine learning algorithms, a Vision Transformer–based variational autoencoder to reduce the design dimensionality of a freeform structure by 3–4 orders of magnitude, and a Bayesian search to form a surrogate model for the input-output relation with huge reduction in computational complexity while avoiding weak local optima.