A mask optimization method based on self-calibrated convolutions is proposed in this paper to reduce the imaging distortion caused by optical proximity effect(OPE). The network model was constructed by combining the inverse lithography technology(ILT), and the parameters of the network model were optimized by the dataset for training. The dataset includes the target pattern and the mask optimized by gradient descent method. The network model based on selfcalibrated convolutions can output an optimized mask according to the target pattern, and the optimized mask is passed through the lithography forward model to obtain the exposure pattern. By the simulation experiment, compared with the traditional gradient-based method, proposed method in this paper has high computational efficiency and small error.
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.