Efficient optical coating design using an autoencoder-based neural network model

Optical thin-film coatings are integral to modern photonics and in particular to ultrafast lasers,providing precise control of dispersion and reflectivity, thus enabling tailored pulse shaping.Designing these coatings represents an inverse problem, requiring the mapping of desired opticalproperties to physical designs, a task that poses major challenges for traditional heuristic methods,which often rely on time-consuming, expert-guided iteration and can be constrained by the choiceof an initial starting point. Here, we present an artificial intelligence (AI) framework for opticalthin-film coating design that accelerates the design process, achieving excellent performance characteristics without expert intervention. We discuss our AI approach and demonstrate the capabilities of our algorithm by designing a complex broadband high-reflectivity mirror with state-ofthe-art performance characteristics including a −200fs2