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.

For more additional details, check out the full article:

To learn more

Share / Subscribe

Related Posts

Are you well “eQYPt”?

Pharma manufacturing success depends on enhancing the most essential KPIs such as quality, yield, and productivity to accelerate time-to-market (eQYPt)...

Achieve critical factory KPIs with industry-proven turnkey CIM solution

Integrate automation capabilities across your entire factory domain with our SmartFactory CIM solution.

Achieve faster resolution of SPC and FDC violations

Extend automation intelligence capabilities in your factory with cohesive OCAP solution using SmartFactory Knowledge Advisor.