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Graduate Defense: Fataneh Jenabi
June 27 @ 10:00 am - 11:00 am MDT
Title: Process-Property Linkages Construction for Inkjet Printing with Machine Learning
Program: Master of Science in Electrical and Computer Engineering
Advisor: Dr. Harish Subbaraman, Electrical and Computer Engineering
Committee Members: Dr. Kurtis Cantley, Electrical and Computer Engineering, and Dr. David Estrada, Materials Science and Engineering and Electrical and Computer Engineering
Printed electronics are emerging technologies that can potentially revolutionize the manufacturing of electronic devices. One promising technology for printed electronics is inkjet printing. Inkjet printing offers both low-cost processing and high resolution. Being a subset of additive manufacturing technologies, inkjet printing minimizes waste and is compatible with a wide range of inks. However, the inkjet printing of electronic devices is still in its infancy. One major challenge for inkjet printing is the complexity of the process optimization and uncertain high throughput production. To achieve a high-quality print, there is a complex parameter space of materials and processing parameters that need to get optimized. To address this challenge in this thesis, we develop a machine learning algorithm to connect the processing parameters to print morphology for inkjet processes. To achieve this goal, we developed more than 200 experimental samples and processed the print images automatically with OpenCV-based codes. Finally, we correlated the morphology specifications, i.e., line width, overspray, and roughness, to processing parameters via different machine learning algorithms.