2024
DOI: 10.1038/s41598-024-57315-4
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Stochastic and multi-objective design of photonic devices with machine learning

Paolo Manfredi,
Abi Waqas,
Daniele Melati

Abstract: Compact and highly performing photonic devices are characterized by non-intuitive geometries, a large number of parameters, and multiple figures of merit. Optimization and machine learning techniques have been explored to handle these complex designs, but the existing approaches often overlook stochastic quantities. As an example, random fabrication uncertainties critically determines experimental device performance. Here, we present a novel approach for the stochastic multi-objective design of photonic device… Show more

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