Applying Discriminant Analysis for Data-Driven Decision Making to Reduce Defects in Integrated Circuit Pick-Up from Dicing Tape: A Case Study of an Electronics Company
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Abstract
An electronics manufacturer experienced a high failure rate in picking up integrated circuits (ICs) from dicing tape at 43.5 percent, leading to increased waste and production costs. This research aimed to investigate the factors affecting the IC pick-up process by applying discriminant analysis to classify the workpieces into two groups: good and defective. The study also sought to identify the optimal values of key variables contributing to defect reduction in the inspection and packaging processes. A total of 180 actual production data sets were used to build and validate the model. Predictor variables included needle distance (X1), vacuum suction force of the pick-up head (X2), and vacuum suction force of the dicing tape needle (X3). The analysis revealed that the two key factors—needle distance and vacuum suction force of the pick-up head—significantly influenced the success of the process. The model achieved 95.14 percent accuracy in training and 97.22 percent in testing. The optimal settings were 502 micrometers for X1 and -0.55 millibar for X2. Additionally, the suction force of the dicing tape needle (X3) was recommended to be set at -0.15 millibar. This resulted in an average proportion of IC pick-up failures decreased by 13.25 percent and the average number of IC that failed to pull up decreased by 43.36 percent that leads to a disposal cost reduction by 48.25 percent.
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