Intelligent defect inspection of flip chip based on vibration signals and improved gcForest

Abstract

The defect inspection of flip chips has become a meaningful and challenging task with the decrease of size and distance of solder balls. In this paper, we proposed a novel defect diagnostic method for flip chips based on vibration signals and improved multi-grained cascade forest (gcForest). Firstly, the flip chip is excited by an air-coupled capacitive ultrasonic transducer, and the corresponding vibration signals of flip chip are captured by a laser scanning vibrometer. Then, the feature information of original vibration signals is extracted by muti-grained scanning (MGS) automatically. Finally, the extracted feature information is input into the cascaded forest for defect diagnosis. Considering the low data transmission efficiency and feature redundancy between MGS and cascaded forest, a feature extraction channel strategy based on kernel principal component analysis (KPCA) is introduced into the cascaded forest. Besides, in order to improve the generalization ability of the improved gcForest, the classifiers of each layer in cascade forest are upgraded. The results demonstrate the superiority of the improved gcForest over the traditional methods on experimental vibration data of flip chips, especially under small training sizes, which is very beneficial to practical engineering.