Abstract
With non-destructive, simple, and low-priced testing methods (which can be applied to manufacturing processes), we can assure high quality production by applying inspection across all products. Quality assurance in wire bonding is possible by using state diagnosis which utilizes detection of elastic waves close to the bonded joint. We succeeded in detecting elastic waves under difficult conditions and in harsh environments by using a thin AE (Acoustic Emission) sensor developed using AlN piezoelectric thin film technology. We attempted to conduct a wire bonding state estimation using the MT (Mahalanobis-Taguchi) method which is a simple and powerful pattern recognition technique. Two issues are raised when applying this method to the manufacturing process: 1) a large probability bias caused by an insufficient number of data samples, and 2) securing homogeneity of the Unit Space. In this paper, we present a state diagnosis method utilizing a real-world application which uses piezoelectric thin film sensing and an improved MT method to which ensemble learning is applied. Performance of this proposed method is also examined through a common benchmark dataset to assure efficient operation in any manufacturing process.
