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摘要
Non-wet solder joints in processor sockets are causing mother board failures. These board failures can escape to customers resulting in returns and dissatisfaction. The current process to identify these non-wets is to use a 2D or advanced X-ray tool with multidimension capability to image solder joints in processor sockets. The images are then examined by an operator who determines if each individual joint is good or bad. There can be an average of 150 images for an operator to examine for each socket. Each image contains more than 30 joints. These factors make the inspection process time consuming and the output variable depending on the skill and alertness of the operator. This paper presents an automatic defect identification and classification system for the detection of non-wet solder joints. The main components of the proposed system consist of region of interest (ROI) segmentation, feature extraction, reference-free classification, and automatic mapping. The ROI segmentation process is a noise-resilient segmentation method for the joint area. The centroids of the segmented joints (ROIs) are used as feature parameters to detect the suspect joints. The proposed reference-free classification can detect defective joints in the considered images with high accuracy without the need for training data or reference images. An automatic mapping procedure which maps the positions of all joints to a known Master Ball Grid Array file is used to get the precise label and location of the suspect joint for display to the operator and collection of non-wet statistics. The accuracy of the proposed system was determined to be 95.8% based on the examination of 56 sockets (76 496 joints). The false alarm rate is 1.1%. In comparison, the detection rate of a currently available advanced X-ray tool with multidimension capability is in the range of 43% to 75%. The proposed method reduces the operator effort to examine individual images by 89.6% (from looking at 154 images to 16 images) by presenting only images with suspect joints for inspection. When non-wet joints are missed, the presented system has been shown to identify the neighboring joints. This fact provides the operator with the capability to make 100% detection of all non-wets when utilizing a user interface that highlights the suspect joint area. The system works with a 2D X-ray imaging device, which saves cost over more expensive advanced X-ray tools with multidimension capability. The proposed scheme is relatively inexpensive to implement, easy to set up and can work with a variety of 2D X-ray tools. Note to Practitioners-The non-wet identification system for solder joints is immediately applicable to all types of PCB sockets. A 2D X-ray machine was set up to hold PCB boards and programmed to image each socket needing inspection. The X-ray collector was set at 45 degrees and the socket was rotated to 45 degrees, 135 degrees, 225 degrees, and 315 degrees. At each rotation a complete set of images was taken of the outer edge of the socket by moving the PCB board in the x and y directions under the X-ray source. These images are input by the software and analyzed and the results displayed to the operator for action. The algorithms were programmed using the Intel (R) Integrated Performance Primitives and Intel (R) Math Kernel Library. The code was then embedded in a C++ program. The code performance was multithreaded and optimized using Intel (R) Threading Building Blocks and Intel (R) Parallel Studio. A user interface was coded and used to display the images processed by the algorithm and found to have a suspect joint. Only the images containing a suspect joint are displayed for the operator to save time. The suspect joints are labeled with an "x" on the displayed image. The operator can click on the suspect joint and the image will move to a zoom window where the image will be available for zooming. The exact label of the joint is also displayed to the operator. Images of the joint at the four different angles are also displayed on the interface as are sample defects for the operator to compare to the current image. There is an input mechanism for selecting defective and non-defective joints in the image. After processing for the image has been completed, defect parametric data is stored into a file. A screen shot of the interface is provided in Fig. A.
- 出版日期2011-1
