Practical example from the SPIE short course "Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization"
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Updated
Feb 25, 2018 - Jupyter Notebook
Practical example from the SPIE short course "Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization"
We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.
Lithography defect prediction for microchip manufacturing optimization with machine learning model
Calculating semiconductor chip yield against defect density using a Monte Carlo simulation is a common approach to assess the impact of defects on chip manufacturing. In this simulation, we'll randomly generate defect locations and evaluate chip yield based on specified criteria.
A Minecraft mod adding realistic semiconductor manufacturing.
Semiconductor Process Control (ECE6455-A) @ Georgia Institute of Technology, Atlanta, GA, USA
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