19 March 2015 Verification of directed self-assembly (DSA) guide patterns through machine learning
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Abstract
Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n-dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology.
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Seongbo Shim, Seongbo Shim, Sibo Cai, Sibo Cai, Jaewon Yang, Jaewon Yang, Seunghune Yang, Seunghune Yang, Byungil Choi, Byungil Choi, Youngsoo Shin, Youngsoo Shin, } "Verification of directed self-assembly (DSA) guide patterns through machine learning", Proc. SPIE 9423, Alternative Lithographic Technologies VII, 94231E (19 March 2015); doi: 10.1117/12.2085644; https://doi.org/10.1117/12.2085644
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