Translator Disclaimer
19 March 2015 Verification of directed self-assembly (DSA) guide patterns through machine learning
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Seongbo Shim, Sibo Cai, Jaewon Yang, Seunghune Yang, Byungil Choi, and Youngsoo Shin "Verification of directed self-assembly (DSA) guide patterns through machine learning", Proc. SPIE 9423, Alternative Lithographic Technologies VII, 94231E (19 March 2015);

Back to Top