Semiconductor manufacturing is not only one of the most technologically advanced sectors, but it’s also among the most cost-intensive. As semiconductor-based devices have become commonplace in everything from personal computers to phones and cars, demand has continued to increase. As volumes increase, the need for more robust quality initiatives rises. While most manufacturers are using statistical software, like Minitab, to solve certain problems, there remains an opportunity to expand their scope and deliver more value.
The good news? Semiconductor manufacturing tends to collect more data on average than other industries. That means you can more easily put your data to work in different ways, such as:Use Measurement System Analysis To Minimize Variation in Production
Using tools like gage R&R and ANOVA to determine variation in the measurement system is critical, particularly for semiconductor manufacturing. To ensure that specifications can be guaranteed, repeatability and reproducibility of measurements needs to be small relative to the measured specification tolerances. Minitab’s new Measurement System Analysis module enables practitioners at all levels to assess measurement system variation, bias and stability with ease.
Use Statistical Process Control To Improve Yields and Avoid Waste in The Fabrication Process
Using control charts and capability analysis to measure critical characteristics like wafer thickness, deposition rates (the rate of depositing material on the wafer surface as a thin layer to contain electrical properties), endpoint times (to detect the most accurate time to stop the etch process in order to avoid over or under etch), among others will help ensure that your process and equipment are in control. If you’re already employing SPC methods, using Minitab’s next generation of statistical process control can help you improve your techniques and deliver real-time savings.
Use Design of Experiment to Improve Manufacturing Processes
Because semiconductor manufacturing is comprised of multiple complex processes, even the most experienced and competent engineers may not necessarily know the best settings for the manufacturing equipment. Even if the optimal settings are known, new technologies are constantly being adopted which introduce unknown situations and new problems. Design of Experiments help engineers build a comprehensive model to help understand, very precisely, how the system works. Learn more about DOE in action that helped to improve the degree of uniformity in one manufacturer’s polishing process by reading this blog post or more about DOE in general by watching this webinar.
Use Machine Learning for Post Silicon Validation
Unlike production testing where you’re taking measurements and making pass-fail decisions, in post silicon validation you need to understand in great detail the behavior of the device under all kinds of operating conditions. Using machine learning, you can better understand how the inputs of the device impact the outputs and find hidden relationships and complexities between them. With Minitab’s Predictive Analytics module, you can build a robust predictive model or use tools like our variable importance chart to highlight the most critical inputs that impact performance.
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