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

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

MEMS patent

Data-Driven Optimization of Semiconductor Processes and Forecasting

Czochralski Apparatus for Modeling Efficient Lattice growth

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

Revolutionary ultra-smooth nanofabrication platform with quantum-enhanced precision manufacturing. Achieved 90.4% manufacturing readiness with full commercial deployment approval. Features quantum coherence positioning (0.062 nm accuracy), real-time quality control, and comprehensive UQ resolution for semiconductor and quantum device manufacturing.

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.

Micron AI Challenge 2025

klayout square array of auto-labeled alignment marks