Technical Library | 1999-08-27 09:29:49.0
Contract packaging houses have to contend with a large mix of die types and products. Flexibility and quick turnaround of package types is a must in this industry. Traditional methods of die encapsulation, (i.e., use of transfer-molding techniques), are only cost effective when producing a large number of components. Liquid encapsulants now provide similar levels of reliability1, and are cost effective...
Technical Library | 2020-02-18 09:56:24.0
Glob Top, Dam and Fill & Flit Chip Underfill To protect PCBs from damaging outside influences, they are coated with a thin layer of casting resin or protective finish during the conformal coating process. In addition to sealing the entire circuit board, it is possible to pot only sections or individual components on the substrate. Different methods ranging from "glob top" to "dam and fill" and "flip chip underfill" have been developed for this purpose.
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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