A technical paper titled “Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks” was published by researchers at Chemnitz University of Technology (Germany).
According to the paper, “this contribution introduces a novel hybrid multistage system of stacked deep neural networks (SH-DNN) which allows the localization of the finest structures within pixel size via a classical computer vision pipeline, while the classification process is realized by deep neural networks.”
Find the open access technical paper here. Published January 2022.
Schlosser, T., Friedrich, M., Beuth, F. et al. Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. J Intell Manuf 33, 1099–1123 (2022). https://doi.org/10.1007/s10845-021-01906-9. Open Access license.
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