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

MEMS patent

A Minecraft mod adding realistic semiconductor manufacturing.

Semiconductor Process Control (ECE6455-A) @ Georgia Institute of Technology, Atlanta, GA, USA

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.