Paper
7 August 2023 New solutions to Cooke triplet problem via analysis of attraction basins
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Abstract
Optical systems in lithography machines play a significant role in their performance and, therefore, need to be optimized for specific applications. Artificial Intelligence (AI) and, in particular, metaheuristics are utilized in optimization algorithms for finding a diverse set of feasible and high-performing designs. The diversity requirement of the produced solutions is enforced to allow taking into account additional constraints that are difficult to formalize. In this work, we analyse the space of solutions previously produced by a niching evolutionary algorithm for the Cooke Triplet optical system and propose an approximation of the manifold where all high-performing designs exist. First, we show the existence of high-performing optical designs that are structurally different from the 21 previously known theoretical solutions. In order to do this, we develop a general computationally efficient methodology to create a partition of known high-quality points and their (accidentally found) improvements to their corresponding attraction basins, in the case when neither the exact number of landscape attractors nor their locations are known. We construct a manifold estimation which contains high-performing solutions by fitting a Gaussian Process-based classifier which predicts if an arbitrary design is close to high-performing. The proposed approach shows that AI-assisted optimization is beneficial, and it can be used to extend the capabilities of lithographic scanners and metrology equipment. Furthermore, it opens the possibility of studying other industrial applications.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kirill Antonov, Tiago Botari, Teuss Tukker, Thomas Bäck, Niki van Stein, and Anna V. Kononova "New solutions to Cooke triplet problem via analysis of attraction basins", Proc. SPIE 12624, Digital Optical Technologies 2023, 126240T (7 August 2023); https://doi.org/10.1117/12.2675836
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KEYWORDS
Mathematical optimization

Evolutionary algorithms

Optical design

Education and training

Lenses

Algorithm development

Optics manufacturing

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