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Rotating x-ray detector Feature extraction and rules-based defect detection Center Toe Heel Toe Quantitative dimensional measurements
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FIGURE 53.12 Schematic of cross-sectional x-ray automated inspection system for solder joint measurement. Adding images around a circle from a rotating x-ray beam and detector creates a focal plane that captures just the solder joints of interest and nothing else below or above.
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the entire joint. Figure 53.13 shows an example of these calibrated measurements. This figure includes the actual cross-sectional x-ray image of tape automated bonded (TAB) solder joints. The profile shown at the top of the x-ray image is generated by the system in physical dimensional units by interpreting and calibrating the grayscale readings of pin 193 in the x-ray image. The table below the x-ray image includes example measurements for both pin 193 and pin 194. Analysis of these physical thickness measurements of solder joints provides the information required for process characterization and improvement. For instance, variations in average solder thickness or volume for the solder joints across a single assembly or from assembly to assembly provide insight into the quality level of the paste printing process as well as sources of defects. The image-processing software then finds and measures solder joint features and flags defects accordingly.
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Advantages and Disadvantages of X-Ray Inspection X-ray solder joint inspection systems can reach average inspection speeds of around 50 to 150 joints per second. X-ray solder joint inspection systems also have higher prices, typically about 50 to 100 percent more than the price of the optical solder joint systems with the fastest inspection speed capability.
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Tape-automated bond (TAB) 2.20
Fillet regions
Pin 194 Pin 193
Good board (pin 6) Reference designator U1 pin 193 Inspection point Pad Heel Center Toe Pad Heel Center Toe Thickness (in 0.001") 0.59 1.18 0.69 1.34 0.58 1.20 0.68 1.30
U1 pin 194
FIGURE 53.13 Cross-sectional x-ray image of TAB solder joints. Image-processing software converts the grayscale readings of the image for pin 193 into the side profile of solder thickness shown above the image. The actual calibrated measurements of average solder thickness across the pad, heel fillet height, center thickness, and toe fillet height processed from the images of pins 193 and 194 are shown in the table below the x-ray image. These measurements indicate that both of these solder joints are good.
Automated x-ray inspection of solder joints has the following major advantages:
Its defect detection capability is extremely high. It eliminates visual inspection by automating solder joint defect detection, thereby also reducing unnecessary rework due to false reject calls. It reduces rework analysis time by pinpointing defects to the exact solder joint.
It affords real-time process control of all three process steps paste printing, component placement, and solder reflow to lower defect rates and rework costs. It provides quantitative measurements to help permanently eliminate the causes of defects from all three process steps. It reduces failures at final assembly and in the field due to defective hidden solder joints and marginal solder joints due to insufficient solder or misalignment or excessive voids. During lead-free conversion, it can be used with minimal program tuning. Automated inspection of solder joints has the following limitations:
Test throughput is not always fast enough to inspect all solder joints within the manufacturing cycle time for the printed circuit assembly. A significant learning curve is required to become expert at developing solder joint tests with both low false accept and false reject rates.
Successful implementation of automated inspection systems into printed circuit assembly production lines requires a significant investment in training, process analysis, and system integration. The implementation can be a lengthy process that requires concerted effort by engineers or skilled technicians. Listed here are highlights of what several manufacturers have learned are key aspects of successfully implementing automated inspection systems:
Assess requirements carefully Start by carefully assessing the requirements for automated inspection in the particular production environment into which the system will be integrated. Determine exactly what kinds of defects are most important for the inspection system to detect, which measurements will most help with process improvement, and what benefits will generate the quickest financial return on investment.3 This assessment must consider the testing and measurement capability that already has been implemented as well as new requirements arising from future printed circuit assembly designs. Evaluate a select set of systems thoroughly Select a small number of automated inspection systems to evaluate thoroughly and compare them against the system requirements. The evaluation should include a benchmark using printed circuit assemblies from production to determine the system s capabilities to detect accurately the important defect types within the required false reject rate, repeatedly make the required measurements, and not exceed the required test time. Elements of cost of ownership should be well understood, including test development time, maintenance skills and cost, expected system downtime, supplier support infrastructure, and supplier maintenance services and prices. Plan for factory system interfaces Consider and plan carefully for interfaces to other factory systems. These systems include board-handling equipment, bar code reading systems, computer-aided design (CAD) systems for automatic download of board layout and component package information, and quality data management systems for SPC and historical quality tracking. Focus on SPC measurements Start with a focus on SPC measurements instead of defect detection. Until the process variation is reduced, most manufacturers will encounter either a false reject rate or a false accept rate that is higher than desired. Allowing one rate or the other to be too high while focusing on reducing the process variation first avoids time-consuming, unproductive tweaking of acceptance thresholds. Reducing process variation requires correlating measurements to the process parameters causing the variation and defects, and then properly adjusting these process parameters.4
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