The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented chips the extraction of chip segmentation lengths, as well as minimum and maximum chip thicknesses. To automatically decide whether a chip at hand should be evaluated using the proposed methods for continuous or segmented chips, a convolutional neural network is proposed, which is trained using supervised learning with available images from embedded chip cross-sections. Data from manual measurements are used for comparison and validation purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.