Automated detection and classification of printing sub-resolution assist features using machine learning algorithms

Kohli, Kriti; Jobes, Mark; Graur, Ioana

doi:10.1117/12.2261417

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“…Preparation of sample training data is a challenge in these applications. Printed SRAF samples are obtained through scanning electron microscope (SEM) images, which should be carefully captured, measured, and classified [39]. Hotspot patterns as well as printed SRAF samples are often scarce, even though the success of machine learning model heavily relies on the coverage of training samples.…”

Section: Discussionmentioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

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Machine learning models have been applied to a wide range of computational lithography applications since around 2010. They provide higher modeling capability, so their application allows modeling of higher accuracy. Many applications which are computationally expensive can take advantage of machine learning models, since a well trained model provides a quick estimation of outcome. This tutorial reviews a number of such computational lithography applications that have been using machine learning models. They include mask optimization with OPC (optical proximity correction) and EPC (etch proximity correction), assist features insertion and their printability check, lithography modeling with optical model and resist model, test patterns, and hotspot detection and correction.

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Section: Discussionmentioning

confidence: 99%