We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.

Practical example from the SPIE short course "Data Analytics and Machine Learning in Semiconductor Manufacturing: Applications for Physical Design, Process and Yield Optimization"

Lithography defect prediction for microchip manufacturing optimization with machine learning model

MEMS patent

Czochralski Apparatus for Modeling Efficient Lattice growth

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.

Data-Driven Optimization of Semiconductor Processes and Forecasting

A Minecraft mod adding realistic semiconductor manufacturing.