Abstract
Semiconductor components’ failure is one of the main causes for spacecraft malfunction. The quality control section has spent every effort to improve the quality of electronic parts and components by inline inspection and offline screening. However, there are products with unapparent failure mechanisms, which will slip through the quality control process and become a potential failure mode in application. The data collected in the semiconductor manufacturing process, including product design, material preparation, assembly, quality control, inception and screening, is highly nonlinear and multidimensional, making data mining an effective tool in processing industrial data, such as defects detection and fault diagnoses. Previous work has been done in improving the semiconductor quality based on data mining method, by specific functions like quality description, quality prediction and quality classification. In this work, the applications of data mining for semiconductor quality control were reviewed and the trends and challenges are analyzed.
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Li, J., Zhang, H., Wang, Y., Cui, H. (2020). A Review of the Applications of Data Mining for Semiconductor Quality Control. In: Wang, Y., Fu, M., Xu, L., Zou, J. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-15-4163-6_58
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DOI: https://doi.org/10.1007/978-981-15-4163-6_58
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