31 March 2014 Effective simulation for robust inverse lithography using convolution-variation separation method
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As critical dimension shrinks, pattern density of integrated circuits gets much denser and lithographic process variations become more pronounced. In order to synthesize masks that are robust to process variations, the average wafer performance with respect to process fluctuations is optimized. This approach takes into account process variations explicitly. However, it needs to calculate a large number of optical images under different process variations during its optimizing process and thus significantly increases the computational burden. Most recently, we proposed a convolutionvariation separation (CVS) method for modeling of optical lithography, which separates process variables from the coordinate system and hence enables fast computation of optical images through a wide range of process variations. In this work, we detail the formulation of robust inverse lithography making use of the CVS method, and further investigate the impacts of arbitrary statistical distribution of process variations on the synthesized mask patterns.
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Wen Lv, Wen Lv, Shiyuan Liu, Shiyuan Liu, Xinjiang Zhou, Xinjiang Zhou, Haiqing Wei, Haiqing Wei, "Effective simulation for robust inverse lithography using convolution-variation separation method", Proc. SPIE 9052, Optical Microlithography XXVII, 90522C (31 March 2014); doi: 10.1117/12.2046322; https://doi.org/10.1117/12.2046322

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