Shear Force Classification Before Wire Bonding Based on Probe Mark 2-D Images Using Machine Learning Methods

The wire bonding process is one of the most critical processes in semiconductor packaging. The electrical performance and reliability of IC chips must be evaluated by probe testing before wire bonding. In the probe test, the probe usually leaves traces on the surface of the pad. Large probe marks tend to cause a decrease in bond adhesion and so increase the possibility of ball bond lifting from the pads. Shear force is an important measure of the adhesion between the ball and pad. The prediction of the shear force helps to understand the bonding quality in advance. In this study, the six features of the probe marks were extracted by the automatic image recognition method. Using three machine learning techniques (logistic regression, support vector machine, and random forest) based on principal component analysis (PCA), the shear force is estimated based on six features before wire bonding. The results indicate that the proposed PCA-based random forest could identify bad chips with 97.92% accuracy before wire bonding. Unnecessary chips can be found early, and unnecessary wire bonding can be avoided, thereby saving process time and cost. The proposed method would improve the efficiency and quality yield of the semiconductor packaging process.