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

Abstract 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 optical properties 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 choice of an initial starting point. Here, we present an artificial intelligence (AI) framework for optical thin-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-of-the-art performance characteristics including a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>200</mml:mn> <mml:mstyle scriptlevel="0"/> <mml:msup> <mml:mtext>fs</mml:mtext> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> group delay dispersion covering a spectral range of 940 nm to 1120 nm.