Address fab-process-related issues to meet quality targets. Statistically analyze test data from multiple operations, utilizing proven PAT (Part Average Testing) algorithms to identify good outlier units. Power up OptimalPlus skills by learning how to execute Outlier Detection algorithms on test data using a pre-defined population. Explore how to integrate and combine these algorithms with a single rule "recipe" during multiple operations. Learn how to automatically switch units to different bins when the rule is applied in production.
Virtual training not available for this course
Classroom training not available for this course
Explain the value of the Outlier Detection solution
Distinguish between the way that each algorithm works
Design different virtual operation rules
Analyze rule results after applying an Outlier Detection algorithm
Instructor-led Classroom: 3-4 days
Customers in the Semiconductors industry
For those who are responsible for increasing product quality and reliability
Quality Engineers, Test & Product Engineers, IT System Administrators, and key users for enhanced training
Prior knowledge of the Global Operations learning path
An environment where the learner can practice
Gathered desired use cases to be covered in class
Provided information if the customer has a Vertica environment
OptimalPlus SW
NI virtual instructor-led training is delivered through Zoom, and Amazon AppStream/LogMein access is provided to participants to perform the exercises on virtual machines equipped with the latest software
Please contact the Customer Success Manager or the Application Support team for more information or to request the course outline.
If you are planning to take three or more NI instructor-led courses within one year, a Training Membership provides cost-effective, unlimited access to all NI public classroom and public virtual courses, along with unlimited certification vouchers.