Machine Learning and AI Data Analytics for the Semiconductor Industry

Semiconductor manufacturing is complex with multiple processes, multiple machines, and multiple process parameters. It is difficult to identify the root cause of defects among these. Manufacturing companies are lacking capability for an earlier process anomaly detection; Second challenge is that they use high cost of Design of Experiments (DOE) design to optimize the process parameters for yield improvement. Another challenge is that poor machine maintenance may cause product quality issue and yield lost. Current scheduled/preventative maintenance may cause unexpected machine breakdown or unnecessary maintenance which affects machine availability. Semiconductor manufacturing needs AI approaches to overcome these challenges.

This course is customized for semiconductor participants and covers a set of methodologies using advanced data analytics in Artificial Intelligence/Machine learning (AI/ML). The training covers both the AI fundamental technologies and hands-on sessions using python programming. Case studies related to semiconductors will be shared during the course. Participants suited for this course include Engineers and managers from semiconductor manufacturing process, quality control department, semiconductor equipment manufacturers and suppliers, and equipment maintenance team.

The course is highly practical and intensive with latest AI knowledge and up-to-date technology, and hands-on with semiconductor example data. Participants will get trained on AI data analytics concepts and can try out their data using our developed sample code. They can then identify potential real industry areas for yield or machine effectiveness improvement.

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