Technical Library | 2007-11-29 17:20:31.0
Programs have been developed to predict the expected yield of flip chip assemblies, based on substrate design and the statistics of actual manufactured boards, as well as placement machine accuracy, variations in bump sizes, and possible substrate warpage. These predictions and the trends they reveal can be used to direct changes in design so that defect levels will fall below the acceptable limits. Shapes of joints are calculated analytically, or when this is not possible, numerically by means of a public domain program called Surface Evolver. The method is illustrated with an example involving the substrate for a flip chip BGA.
Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
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