Abstract
This paper proposes the use of a genetic algorithm to optimize mask and illumination geometries in optical projection lithography. A fitness function is introduced that evaluates the imaging quality of arbitrary line patterns in a specified focus range. As a second criterion the manufacturability and inspectability of the mask are taken into account. With this approach optimum imaging conditions can be identified without any additional a-priori knowledge of the lithographic process. Several examples demonstrate the successful application and further potentials of the proposed concept.
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Fühner, T., Erdmann, A., Farkas, R., Tollkühn, B., Kókai, G. (2004). Genetic Algorithms to Improve Mask and Illumination Geometries in Lithographic Imaging Systems. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_22
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DOI: https://doi.org/10.1007/978-3-540-24653-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21378-9
Online ISBN: 978-3-540-24653-4
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