New solutions to Cooke triplet problem via analysis of attraction basins

Citations Over Time

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.

Related Papers

• → A fine-grained load balancing technique for improving partition-parallel-based ontology matching approaches(2016)8 cited
• → Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic(2017)3 cited
• Research Based on the IP address subnetting algorithm(2012)
• Implementation for Distributed Ada Language System(2000)
• → Improving Automatic Classifiers Through Interaction(2016)