Boosting Clip Attach Vision Precision in Semiconductor Production

Key Takeaways

The OSAT sector is crucial in semiconductor manufacturing, directly affecting device quality and reliability.
Utilizing the Six Sigma DMAIC approach significantly reduced clip lifting defects due to improper solder paste application.
After implementing optimized vision detection settings, defect rates fell from 5.6% to 0%, enhancing product quality and customer satisfaction.

The semiconductor assembly and test (OSAT) sector is vital to the electronic manufacturing ecosystem, focusing on the assembly, testing, and packaging of devices. A key quality issue identified in this industry is clip lifting during assembly, often caused by insufficient solder paste beneath the clip. This problem leads to increased rejection rates and jeopardizes manufacturing efficiency.

To address clip lifting, the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology was implemented. This structured approach helps identify inefficiencies and enhance product quality. Reducing clip lifting not only decreases rework costs but also improves customer satisfaction and trust.

The study started with the Define phase, which clarified the project scope based on X-ray analysis of defect types. From the initial evaluations using a Pareto chart, clip lifted defects accounted for 26.9% of overall faults. These defects are critical as they are detectable only through X-ray, revealing insufficient solder volume on the die.

In the Measure phase, thorough data collection focused on identifying the root causes of clip lifting. A detailed process flow mapping indicated that the clip attach phase was crucial, particularly before curing, where abnormal solder defects often occurred. Utilizing cause-and-effect diagrams helped identify variables such as “Man, Method, Material, and Machine.”

During the Analyze phase, statistical methods were used to examine the relationship between vision detection settings and clip lifting. A chi-square test indicated a significant association, confirming that vision detection settings impact defect occurrences. The null hypothesis that these settings have no effect was rejected, leading to acceptance of the alternative hypothesis, which confirmed their significance.

In the Improve phase, different vision brightness levels (40, 60, and 80 lux) were evaluated to optimize the solder paste vision detection system. The brightness setting of 60 lux was determined to be optimal, significantly enhancing the accuracy in detecting solder volume, thereby minimizing clip lifting defects.

Finally, the Control phase established measures to sustain these improvements. Training sessions ensured that production staff understood the new vision detection settings, contributing to a drastic reduction in defects. Performance metrics indicated an impressive drop in defect rates, ultimately achieving a continual defect rate of 0%.

This project exemplifies effective problem-solving and process optimization in semiconductor manufacturing, demonstrating how systematic approaches like Six Sigma can drive significant advancements in product quality and reliability.

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