首页|Hongik University Researcher Releases New Data on Machine Learning (Contact Hole Shrinkage: Simulation Study of Resist Flow Process and Its Application to Block Copolymers)
Hongik University Researcher Releases New Data on Machine Learning (Contact Hole Shrinkage: Simulation Study of Resist Flow Process and Its Application to Block Copolymers)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Seou l, South Korea, by NewsRx correspondents, research stated, "For vertical interco nnect access (VIA) in three-dimensional (3D) structure chips, including those wi th high bandwidth memory (HBM), shrinking contact holes (C/Hs) using the resist flow process (RFP) represents the most promising technology for low-k1 (where CD =k1l/NA,CD is the critical dimension, l is wavelength, and NA is the numerical a perture)." Funders for this research include Hongik University. The news correspondents obtained a quote from the research from Hongik Universit y: "This method offers a way to reduce dimensions without additional complex pro cess steps and is independent of optical technologies. However, most empirical m odels are heuristic methods and use linear regression to predict the critical di mension of the reflowed structure but do not account for intermediate shapes. In this research, the resist flow process (RFP) was modeled using the evolution me thod, the finite-element method, machine learning, and deep learning under vario us reflow conditions to imitate experimental results. Deep learning and machine learning have proven to be useful for physical optimization problems without ana lytical solutions, particularly for regression and classification tasks."
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