Experimental Machine Learning for Aperiodic Wafer-Scale Photonics Inverse Design

In this work, we propose a novel framework for large-scale aperiodic nanophotonic inverse design utilizing an experimental machine-learning technique. With this technique, we create an extensive dataset of 10 million experimental structures for enhanced flat-optics design. This largest publicly available inverse design dataset, achieved through electron beam lithography, bypasses the extensive computational demand of first-principle simulations. Experimental acquisition ensures the dataset embodies real-world variances, leading to ML models with a ten-fold improved prediction accuracy in optical responses, drastically reducing validation RMSE from 0.012 to 0.0018. With this dataset, we developed a framework for large-scale aperiodic photonics design capable of designing tens of structures per second. We demonstrate the efficiency of the proposed technique by creating a large (3x3 mm) aperiodic photonic structure composed of > 10000 individual structures with pre-defined transmission/reflection responses.