Pattern selection for OPC (Optical Proximity Correction) model calibration is crucial for high-quality OPC results and low edge placement error (EPE) error in semiconductor fabrication. Pattern coverage check is also desired with the value to identify potential anomaly before mask tape out for monitoring and repair. This study evaluates pattern diversity based selection and pattern coverage check for Extreme Ultraviolet (EUV) C/H mask layers. Pattern diversity based selection has the advantage of incorporating information related to lithographic contrast and illumination effects, offering a more nuanced representation of patterns in a lithographic context. Using unsupervised machine learning, we analyze the lithographic pattern representations from sample designs and select out pattern representatives for OPC model. The study concludes pattern selection and coverage check can enhance model prediction performance for the OPC applications.
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