Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient

The normalized cross correlation coefficient is a prevalent pattern-matching algorithm in machine vision for industrial inspections. Despite its common use, there are problems with practical applications. For instance, false alarms occur since it is highly sensitive to environmental changes or inspection equipment, not to mention it requires complex calculations. This paper proposes the partial information correlation coefficient (PICC) method to improve the traditional normalized cross correlation coefficient (TNCCC). The PICC uses the technique of significant points to calculate the correlation coefficient. An experiment is also conducted to demonstrate the application through many image samples from the IC industry, such as PCBs, BGAs, and ICs. The results show that the PICC can effectively reduce false alarms in defect detection.