Source mask optimization using real-coded genetic algorithms

Chaoxing Yang; Xiangzhao Wang; Sikun Li; Andreas Erdmann

doi:10.1117/12.2010137

12 April 2013 Source mask optimization using real-coded genetic algorithms

Chaoxing Yang, Xiangzhao Wang, Sikun Li, Andreas Erdmann

Proceedings Volume 8683, Optical Microlithography XXVI; 86831T (2013) https://doi.org/10.1117/12.2010137
Event: SPIE Advanced Lithography, 2013, San Jose, California, United States

Abstract

Source mask optimization (SMO) is considered to be one of the technologies to push conventional 193nm lithography to its ultimate limits. In comparison with other SMO methods that use an inverse problem formulation, SMO based on genetic algorithm (GA) requires very little knowledge of the process, and has the advantage of flexible problem formulation. Recent publications on SMO using a GA employ a binary-coded GA. In general, the performance of a GA depends not only on the merit or fitness function, but also on the parameters, operators and their algorithmic implementation. In this paper, we propose a SMO method using real-coded GA where the source and mask solutions are represented by floating point strings instead of bit strings. Besides from that, the selection, crossover, and mutation operators are replaced by corresponding floating-point versions. Both binary-coded and real-coded genetic algorithms were implemented in two versions of SMO and compared in numerical experiments, where the target patterns are staggered contact holes and a logic pattern with critical dimensions of 100 nm, respectively. The results demonstrate the performance improvement of the real-coded GA in comparison to the binary-coded version. Specifically, these improvements can be seen in a better convergence behavior. For example, the numerical experiments for the logic pattern showed that the average number of generations to converge to a proper fitness of 6.0 using the real-coded method is 61.8% (100 generations) less than that using binary-coded method.

Citation Download Citation

Chaoxing Yang, Xiangzhao Wang, Sikun Li, and Andreas Erdmann "Source mask optimization using real-coded genetic algorithms", Proc. SPIE 8683, Optical Microlithography XXVI, 86831T (12 April 2013); https://doi.org/10.1117/12.2010137

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available