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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