Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures

Abstract

We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.

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• Illustrated tutorial on global optimization in nanophotonics
• Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology
• Bayesian optimization for state engineering of quantum gases
• Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction