Yukio Matsuyama

Automated solder joint inspection system using optical 3-D image detection

An automated system has been developed for visually inspecting the solder joints of SMDs (Surface Mounted Devices). The system is capable of inspecting fine pitch components down to 0.3 mm pitch QFPs (Quad Flat Packages). A unique image detection method was also developed to obtain precise 3-D images of solder joints. The principle of a confocal microscope is employed but plural sensors are used to detect reflected light at different focusing positions simultaneously. The system is unaffected by secondary reflection and dead angles. The warp in a PC (Printed Circuit) board surface is calculated in real time using the detected 3-D images, and board height to be detected in successive areas is predicted based on this calculation. Real-time automatic focusing control is then performed using newly developed defect detection algorithms, the system can recognize leads, pads and solder fillets from the detected images. Because 3-D shape features are extracted and used for defect judgment, user-defined parameters have been made easy to understand and/or to modify. Operational evaluation of the system confirms a 100% defect detection rate and a very low false alarm rate (0.16%).

Precise Visual Inspection of LSI Wafer Patterns by Local Perturbation Pattern Matching Algorithm

A new defect detection algorithm that compares grayscale images of actual patterns and an automatic visual inspection system implementing this algorithm have been developed. The objective is to detect defects reliably down to 0.3 μm in LSI photoresist patterns on a silicon wafer. To detect defects reliably while remaining uninfluenced by tiny differences between two images of satisfactory patterns, the images are matched in local windows by perturbing one image in the x-y plane and in the brightness direction against the other image. The resulting unmatched regions are recognized as defects. One unique feature of the algorithm is its utilization of polarity changes in the subtracted images during the perturbation for deleting tiny differences between two images. All processing can be done in real time by local, one-pass operators. The developed automatic visual inspection system has achieved a 100 percent detection rate for defects down to 0.3 μm.

Automatic visual inspection system for thin film magnetic head wafer using optical enhancement and image processing techniques

An automatic visual inspection system has been developed for Thin Film magnetic Head (TFH) wafers. Although there are only a few classes of defects to be detected, the difficulty of defect detection varies drastically depending on the location of the defect. When the optical characteristics of a defect and the underlying element pattern are similar, the defect becomes difficult to detect. To detect all defects reliably, we developed the following new techniques. (1) Optical enhancement: The wafer is illuminated by a slit-shaped light source from an oblique direction, and the scattered light is used for detecting flakes in the transparent protection layer. Reflected light from the surface is also used for detecting surface defects. Defects are easily extracted by thresholding the detected image. (2) Image processing : An element-to-element comparison method is employed to detect defects that cannot be enhanced optically. Many bright spots within the ceramic substrate cause discrepancies when compared. Local minimum processing is used for eliminating these and stabilizing defect detection. The system has been evaluated in an actual production line and the defect detection rate achieved is approximately 13% higher than human performance.