Role of statistical process control in semiconductor yield management
There is a boom in the volume of semiconductor devices being manufactured, and the boom is primarily credited to the proliferation of Internet of Things (IoT)-based devices in our daily lives. IoT devices are generally referred to as semiconductor or electronic devices that are nontraditional, connected (able to communicate), and smart (able to process and compute). These include new smart LED TVs, Wi-Fi-enabled air conditioners, and other connected devices containing sensors and processors. Heard of self-driving cars? It is all possible because of the advancement in primary disciplines like semiconductor device manufacturing, artificial intelligence (AI) and machine-learning algorithms. This heavy reliance of semiconductor-based devices in our lives has resulted in the sheer volume of devices being manufactured by semiconductor companies.
This reliance brings us to a very important aspect: are these devices reliable or not? Are the semiconductor manufacturing companies ensuring quality and reliability? What if a task-critical semiconductor-based device stop to perform in the field? There are no simple answers to these questions as semiconductor manufacturing is not a 100% defect-free process, but with the help of the latest quality-control and analytical systems, the defect rates for top manufacturers have been reduced from “defects per million” to “defects per billion,” making these systems highly reliable and the manufacturing process highly efficient.
This brings us to another very important aspect—how are semiconductor manufacturing companies ensuring that quality and reliability metrics are met when the volumes are increasing day by day? Fortunately, the answer to this question is relatively simpler, owing to things: the statistical analysis and data analytics capabilities of semiconductor yield-management systems or software.
Semiconductor manufacturing is a complex and capital-intensive process. The manufacturers want to get higher returns on their investment and push the manufacturers to reduce the downtime without affecting the quality of the products at the same time. Semiconductor manufacturing involves a lot of steps starting from selecting dies to final testing of the packaged IC or device, and during each node a huge amount of data is produced and captured by the data-analytics and yield-management software. The focus is now shifting towards semiconductor wafer data and applying statistical techniques to sample them. The most common technique being employed is statistical process control in semiconductor manufacturing industry using powerful statistical-process-control (SPC) software.
Statistical process control for semiconductor industry uses the basic statistical techniques for quality control and to separate outliers from the operation floor that will hamper yield and have a tendency to fail once they are shipped to the customer. It also allows the test engineers to differentiate between normal (systematic) variations and special variations through the use of control limits and charts at each node of the semiconductor manufacturing process, thus improving the quality and reliability of the manufacturing cycle. The biggest advantage of using statistical process control for semiconductor manufacturing is to achieve early warning of systematic and special issues before producing a lot of potentially bad devices.
See related articles:
- Part average testing’s popularity grows in the semiconductor industry
- Wafer-mapping software in modern-day high-volume manufacturing
- RMAs and yield management systems in semiconductor manufacturing
- Engineering data and the economics of semiconductor manufacturing
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