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Advancing X-Ray Scattering Metrology Using Inverse Genetic Algorithms



Adam F. Hannon, Daniel F. Sunday, Donald A. Windover, Regis J. Kline


We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov Chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean- absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.
Journal of Micro/Nanolithography, MEMS, and MOEMS


X-ray scattering, genetic algorithm, covariance matrix adaptation evolutionary strategy, differential evolution, Markov Chain Monte Carlo, nanostructure, metrology


Hannon, A. , Sunday, D. , Windover, D. and Kline, R. (2016), Advancing X-Ray Scattering Metrology Using Inverse Genetic Algorithms, Journal of Micro/Nanolithography, MEMS, and MOEMS, [online], (Accessed April 14, 2024)
Created July 7, 2016, Updated November 10, 2018