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Semiconductor process engineers currently spend almost 10% of their time extracting critical dimensions from microscope images. Images are analyzed one by one, which is tedious, prone to human bias, time-consuming and expensive. Accurate, automated detection of edges and different materials in a stack are the key technical challenges for computer-extracted critical dimensions (CDs). Here we demonstrate the performance of a method for edge detection and material detection via segmentation methods embodied in the software tool Weave™. This-approach uses optimized thresholding via a level set method to identify multiple edges and materials without the need of extensive, annotated, experimental training data. The method is evaluated based on accuracy (prediction of CDs) and materials identification (ability to identify the different materials in an image). Based on evaluation of the method with 20 test SEM images, the method’s performance is excellent. Ninety percent of the CDs measured from the automated analysis are within 2% of the actual values. The errors for the remaining 10% of measurements range from 4-9%.
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