Using Graph Attention for Virtual Metrology in Semiconductor Manufacturing (Intel Foundry, ASU)

Researchers from Arizona State University and Intel Foundry have collaborated on a paper titled "Graph Attention-Based Virtual Metrology for Film Deposition Processes in Semiconductor Manufacturing." The study focuses on the use of artificial intelligence-driven virtual metrology to predict wafer-level characteristics in semiconductor manufacturing. The proposed framework utilizes graph attention mechanisms to model structured dependencies among process variables, improving predictive performance compared to existing models. The approach integrates temporal feature learning and parameter-layer dependency modeling to enhance prediction accuracy and provide meaningful insights into process dynamics, supporting effective monitoring and optimization in semiconductor manufacturing.