AI-Driven Design of High-Performance Optical Thin Film Coatings for Ultrafast Lasers

Ultrafast laser systems critically depend on optical thin film coatings to control dispersion and light propagation, enabling precise shaping of light. Optical thin film coating design represents a complex inverse problem traditionally relying on computationally intensive numerical optimization methods. These conventional approaches, exemplified by the widely used Needle Algorithm [1], require significant computational resources and often rely on expert intervention [2]. We here propose a new approach to these challenges and present a physics-informed machine learning framework based on an autoencoder architecture and use it to design an ultra-broadband dispersive mirror.