Hong-Chuan Chang

During one project, we noticed abnormal fluctuations in product quality, which raised concerns about process stability. Rather than simply reacting to the issue, we took a proactive approach by diving deep into the data. After conducting a thorough analysis, we pinpointed a specific step in the manufacturing process as the root cause. The anomaly was particularly evident in the bottom U%, which showed significant deviations.

To systematically address this, we implemented Statistical Process Control (SPC) to monitor U% variations across different batches. By carefully analyzing historical trends and real-time data, we identified a pattern suggesting that certain process parameters were influencing uniformity. However, to precisely determine the key factors, we designed a series of experiments using the Design of Experiments (DOE) methodology. We systematically tested variables such as flow rate, pressure, and temperature, ensuring that we captured their individual and interactive effects on the process.

Through iterative testing and optimization, we discovered that fine-tuning the Chemical Vapor Deposition (CVD) parameters had a significant impact on improving uniformity. By adjusting the key settings, we successfully reduced the bottom U% from 5.6% to within 1%, while simultaneously ensuring overall process stability.

Beyond just fixing the immediate issue, this optimization had a significant impact on efficiency and cost savings. By enhancing uniformity and maximizing material utilization, we achieved a 40% cost reduction, significantly lowering production costs while maintaining high product quality. The improvements also resulted in better process control, reducing the likelihood of future defects and enhancing overall yield.

This experience reinforced the importance of data-driven problem-solving and process optimization. By combining analytical thinking with a structured experimental approach, we not only resolved a critical issue but also delivered lasting improvements that benefited both productivity and cost efficiency.