THE ELEVENTH IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (IEEE SAM2020)

198 / 2019-12-30 10:47:00

Principal Component Analysis Based Kullback-Leibler Divergence for Die Crack Detection

PCA; data fusion; Gaussian distribution; die crack; K-L divergence; state index

IEEE SAM 2020: Special Sessions > Array Signal Processing with Imperfect Scenarios

Die crack is a vital issue that directly influences the quality of chip assemblies. In this paper, we focus on detecting die cracks using principal component analysis (PCA) and Kullback-Leibler(K-L) divergence. Our method involves data fusion, including three steps: 1) apply PCA to convert high-dimensional data to low-dimensional data; 2) obtain the frequency distribution histograms of the transformed data and fit them; 3) use K-L Divergence based state index to quantitatively evaluate die crack. Our method works very well with real-life data. Die crack is identified according to die crack data showing skewed distribution, while normal data have Gaussian distribution. Moreover, the proposed state index could successfully detect die cracks.