Nondestructive diagnosis of flip chips based on vibration analysis using PCA-RBF. (15th February 2017)

• Record Type: Journal Article
• Authors: Su, Lei; Shi, Tielin; Liu, Zhiping; Zhou, Hongdi; Du, Li; Liao, Guanglan
• Is Part Of: Mechanical systems and signal processing. Volume 85(2017)
• Page Start: 849
• Page End: 856
• Publication Date: 2017-02-15
• Subjects: Flip chip -- Nondestructive diagnosis -- Principal component analysis -- Radial basis function -- Vibration analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.09.030
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Flip chip technology combined with solder bump interconnection has been widely applied in IC package. The solder bumps are sandwiched between dies and substrates, leading to conventional techniques being difficult to diagnose the flip chips. Meanwhile, these conventional diagnosis methods are usually performed by human visual judgment. The human eye-fatigue can easily cause fault detection. Thus, it is difficult and crucial to detect the defects of flip chips automatically. In this paper, a nondestructive diagnosis system based on vibration analysis is proposed. The flip chip is excited by air-coupled ultrasounds and raw vibration signals are measured by a laser scanning vibrometer. Forty-two features are extracted for analysis, including ten time domain features, sixteen frequency domain features and sixteen wavelet packet energy features. Principal component analysis is used for feature reduction. Radial basis function neural network is adopted for classification and recognition. Flip chips in three states (good flip chips, flip chips with missing solder bumps and flip chips with open solder bumps) are utilized to validate the proposed method. The results demonstrate that this method is effective for defect inspection in flip chip package. Highlights: This paper presents a nondestructive diagnosis method for defect detection of flip chips. Forty-two features were extracted from vibration signals for analysis. PCA-RBF was introduced for feature reduction and defect assessment. The results demonstrate the feasibility of this approach for flip chip diagnosis.