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

Sub-Resolution Assist Feature (SRAF) printing is a critical yield detractor and known issue in OPC technology. SRAF print avoidance models can be used to determine where undesirable printing is likely to occur, but such models lack the necessary robustness and reliability for the detection of all SRAF printing cases. Classification of printing SRAFs is a subjective and manual task where many engineering hours are spent. In this work we demonstrate a reliable way to accurately classify images according to SRAF printing risk. Testing multiple sets of data, across multiple processes, yielded a prediction success rate of 97% wherein only a single image was under-predicted. Under-prediction is when a model fails to predict printing SRAFs; a key defect generator, as it means the model will not be able to remove the SRAF shape in the OPC iteration before mask build. We propose a new methodology as to accurately auto-classify and filter images with SRAF printing on wafer. This scalable solution will improve the quality and reliability of SRAF print avoidance models and reduce the risk of printing SRAF by removing the manual, highly subjective, image classification step.