Fast and noise-tolerable image subtraction methods for defect detection on PCBs and LED wafers

• Main Authors: Victor Ham Choi, 趙寶華
• Other Authors: 蔡篤銘
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/99726917509710622081

碩士 === 元智大學 === 工業工程與管理學系 === 97 === Automated inspection of assembled printed circuit boards (PCB) is a requirement to ensure the quality of the product and to decrease manufacturing cost. An assembled PCB comprises complex conductive paths with different electronic components such as integrated circuits (IC), resistors and capacitors, which make template matching the only possible approach for automated optical inspection. Current template-based defect detection techniques use simple image subtraction to identify defects on assembled PCBs which relies on accurate image alignment of the reference and inspection images. If this cannot be achieved, noise points are generated around the edges. Changes in illumination are another factor to consider since image difference will yield false detections. This study is aimed to develop three template-based approaches for defect detection and, especially, focuses on PCBs and light emitting diode (LED) chips. These methods are robust under small misalignments, changes in illumination and manufacturing variation. The first proposed method, named statistical process control (SPC) with multiple templates, uses statistical process control techniques. It takes a number of defect-free images to form a “mean” template, described by the gray-level mean and standard deviation of each individual pixel in the image. This method is faster than the simple image subtraction method, and is tolerant to noise due to misalignments, illumination changes and manufacturing product variation. The second proposed method is a kernel-based scheme. The gray-level difference between the template and the inspection image in a small neighborhood window can be efficiently calculated from a kernel function. The weighted kernel value can then be used for discriminating defect points from the background. The kernel method is also fast enough when compared to the simple image subtraction method and very responsive to detect low-contrast defects. The third method is called hybrid method which combines the SPC with multiple templates and the kernel-based method. The hybrid method takes a number of defect-free images but instead of calculating the “mean” template, it uses these images to calculate the values of the kernel weighting function. The hybrid method is computationally fast and very responsive to the detection of low-contrast defects. All these proposed methods are compared against a benchmark method which is based on image subtraction for defect detection. The benchmark method calculates the minimum value of the pixel intensities between a template image and an inspection image. The minimum value is calculated in a neighborhood window and is used to overcome minor misalignments. Tests are conducted with defect-free and defective PCBs and LED chips under different misalignments and illumination variation. In comparison with the benchmark method, all the proposed methods produce noise-free inspections results. They are computationally fast for on-line real-time defect detection and robust enough to overcome noise due to misalignments, changes in illumination and manufacturing product variation.