SEMI102 Artificial Intelligence for Thin Film Manufacturing

Course Description 

This course describes how machine learning and AI-based approaches to research, development, and production bring advantages to cleanroom processes.  AI-based identification of time-varying equipment performance, and the effects of the previous recipe used on the outcome of the current desired methods, are just some ways AI can reduce process variability.  

The team from Cornell has applied AI approaches to optimize lithography and etching processes involved in the development of an RF wake-up Nano Electromechanical system switch that needs a well-controlled gap between a moving shuttle and a contact. We report on a decision-tree based AI model for predicting lithography outcomes. This work is being applied to plasma etching and the combined prediction of lithography and thin-film etching using CD-SEM imagery for feature extraction and modeling process variables.  Additional approaches to train process-modeling CAD tools to result in better process development experience are developed.

Course Objectives  

• Describes how machine learning and AI-based approaches to research, development, and production bring advantages to cleanroom processes.
• AI-based identification of time-varying equipment performance. 

Course Duration

30 minutes

Target Audience 

Managers, supervisors, engineers, technicians, or any individual working directly with this equipment or product  

Requisite Knowledge 

None