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How to drive process quality using machine learning and expert systems

Learn how this technique has become a cornerstone to build thinking machines into the world of manufacturing.
Have you ever wondered how grocery stores arrange their products? For example, how do they figure out how to display butter so close to the bread? How does Netflix recommend a movie based on our watch history?
These are examples of “shopping basket analyses” based on a data extraction technique commonly referred to as frequent pattern mining. This type of technique looks for recurring relationships to find correlations between different data items. When we apply this technique to semiconductor manufacturing, it helps derive an in-depth understanding of the process to attain rapid quality enhancements. Such algorithms are fully capable of handling high volumes of data with minimal human intervention to push the best-in-class material. Our team at Applied, has adopted machine learning studies, which explicitly expose the behavior of manufacturing tools and processes for customized, continuous, and automated adjustments.

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About the Author

Picture of Vishali Ragam, Global Product Manager, SPC
Vishali Ragam, Global Product Manager, SPC
Vishali has been working in the semiconductor industry for more than 15 years. Prior to joining Applied Materials, she worked at Micron Technology, first as a process engineer and then as a senior quality engineer. She has been with Applied for seven years, having joined the company as a quality solutions architect. Vishali is currently a Global Product Manager overseeing SmartFactory SPC3D, an advanced process control (APC) engine that runs statistics to determine if processes are within spec to improve product yield. Vishali has an MS in mechanical engineering from Oklahoma State University, and a bachelor’s in mechanical engineering from Osmania University, in Hyderabad, Telangana, India.