Calibration pattern coverage is critical for achieving a high quality, computational lithographic model. An optimized calibration pattern set carries sufficient physics for tuning model parameters and controlling pattern redundancy as well as saving metrology costs. In addition, as advanced technology nodes require tighter full chip specifications and full contour prediction accuracy, pattern selection needs accommodate these and consider contour fidelity EP (Edge Placement) gauges beyond conventional test pattern sets and cutline gauge scopes. Here we demonstrate an innovative pattern selection workflow to support this industry trend. 1) It is capable of processing a massive candidate pattern set at the full chip level. 2) It considers physical signals from all of the candidate pattern contours. 3) It implements our unsupervised machine learning technology to process the massive amount of physical signals. 4) It offers our users flexibility for customization and tuning for different selection and layer needs. This new pattern selection solution, connected with ASML Brion’s MXP (Metrology of eXtreme Performance) contour fidelity gauges and superior, accurate Newron (deep learning) resist model, fulfills the advanced technology node demands for OPC modeling, thus offering full chip prediction power.
As the design node of memory device shrinks, OPC model accuracy is becoming ever more critical from development to manufacturing. To improve the model accuracy, more and more physical effects are analyzed and terms for those physical effects are added. But it is unachievable to capture the complete physical effects. In this study, deep neural network is employed and studied to improve model accuracy. Regularization is achieved using physical guidance model. To address overfitting issue, high volume of contour based edge placement (EP) gauges (>10K) are generated using fast eBeam tool (eP5) and metrology processing software (MXP) without increasing turnaround time. It is shown that the new approach improved model accuracy by >47% compared to traditional approach on >1.4K verification gauges.
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.