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Item type:Publication, Access status: without files , A physics-informed AI model for on-demand design of complex optical coatings(SPIE, 2026-05-28); ; ; ; The inverse design of multilayer optical coatings is a computationally intensive, non-convex optimization problem. We address this using a neural network framework trained to directly generate physical layer structures from target spectra. Our approach couples a ‘design’ network with a parameter-free, differentiable Transfer Matrix Method (TMM) model. During training, the network proposes a layer stack, the TMM model calculates its optical response, and the network’s parameters are adjusted to minimize the difference between this response and the target. This end to-end process allows the network to learn the physics of optical design directly without relying on any prior design data. Using this approach, we have generated a broadband dispersive mirror with performance characteristics comparable to those obtained by established commercial optimization tools. Building on this validated approach, we now show that this framework can be used to create a general, pre-trained model that rapidly generates initial designs for new problems, functioning as a powerful tool for optical coating design. Furthermore, the model architecture is inherently adaptable; it can be quickly fine-tuned on specific, user-defined design problems to further enhance its performance. This work demonstrates a practical pathway towards fast, generalizable, and continually improving AI tools for optical system design.Publicationtype: Conference Paper46 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Efficient optical coating design using an autoencoder-based neural network model(IOP Publishing, 2025-11-12); ; ; ; 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 group delay dispersion covering a spectral range of 940 nm to 1120 nm.Publicationtype: Journal Article24 42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , AI-driven design of high-performance optical thin film coatings for ultrafast lasersUltrafast 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.Publicationtype: Conference Paper23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Neural network method for dielectric optical coating design(EDP Sciences, 2024-10-18); ; ; ; We use neural networks to address the challenge of deriving dielectric coating designs from the desired optical properties. We show that our trained neural network can automatically design common laser mirror coatings types efficiently.Publicationtype: Journal Article50 44