It is crucial to predict hard failure in photolithography process to determine design rules and process condition in the product development stage. Accurate prediction of hard failures through simulation have powerful effects such as shortening the product development period and improving mass production yield. Previously, parameters used to determine whether a pattern is expected to fail include NILS (Normalized Image Log-Slope), image contrast, or chemical distribution in the photoresist. However, these methods are almost infeasible because the accuracy becomes low as process condition changes and calibration process of chemical distribution is too complicated. In this paper, a novel method using optical parameters and machine learning is proposed to predict hard failures of ADI (After Development Inspection) patterns, and this methodology was evaluated in the process of applying inorganic photoresist.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.