Flex Electronics Webinar Master Class: May 2021 (on demand)
Fabrication in the cleanroom, and in the newer printed electronics tools, are often a function of time-varying parameters of the equipment, environment, and materials. The parameters often have co-dependencies across different process steps and tool sets. Physics based models and linear regression have been used traditionally, which are often not sufficient to learn the underlying variabilities.
This course will teach the material needed to connect cleanroom and printed electronics science and technology to that of advanced data processing capabilities enabled by Artificial Intelligence and Machine Learning. Cleanroom tools inherently can have millions of internal variables and can learn from the datasets, providing a powerful and complementary approach to traditional feedback control and process stabilization approaches.
Learning models are developed on images (CD-SEMS, optical images), time history data (Optical Emission Spectroscopy), and textual process information. A subset of the class will include: (1) Approaches to preprocess image data and create learning-based models, (2) model verification, (3) application to nanomechanical switch fabrication, and (4) cloud based implementation, data security, and data standardization.
CANCELLATION POLICY: We do not accept cancellations for on demand programming.