OPC recipe optimization using genetic algorithm

Abhishek Asthana; Bill Wilkinson; Dave Power

doi:10.1117/12.2219166

15 March 2016 OPC recipe optimization using genetic algorithm

Abhishek Asthana, Bill Wilkinson, Dave Power

Proceedings Volume 9780, Optical Microlithography XXIX; 97800J (2016) https://doi.org/10.1117/12.2219166
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

Abstract

Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer’s intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex.

The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.

Citation Download Citation

Abhishek Asthana, Bill Wilkinson, and Dave Power "OPC recipe optimization using genetic algorithm", Proc. SPIE 9780, Optical Microlithography XXIX, 97800J (15 March 2016); https://doi.org/10.1117/12.2219166

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