Using GA-SVM for defect inspection of flip chips based on vibration signals

Abstract

Flip chip technology has been widely used in IC packaging, and the combination of flip chip technology and solder joint interconnection technology has been utilized in the manufacturing of electronic devices universally. As the development of flip chip towards high density and ultra-fine pitch, the inspection of flip chips is confronted with great challenges. In this paper, we developed an intelligent system used for the detection of flip chips based on vibration. Thirty-four features including 18 time domain features and 16 frequency domain features were extracted from the raw vibration data. The support vector machine was employed to implement the recognition and classification of flip chips. In order to improve the classification accuracy of SVM, cross validation (CV) and genetic algorithm (GA) were utilized to optimize the parameters of SVM respectively. SVM, CV-SVM and GA-SVM were applied to classification separately and the results were obtained. By comparison, GA-SVM can recognize and classify the flip chips rapidly with high accuracy. Thus, GA-SVM is effective for the defect inspection of flip chips.