Virtual Metrology in Semiconductor Fabrication Foundry Using Deep Learning Neural Networks

Abstract

Physical metrology inspections are crucial in semiconductor fabrication to ensure that wafers are fabricated within the production specification limits and to prevent faulty wafers from being shipped and installed in customer devices. However, it is not possible to examine every wafer, as such inspection would incur impractical costs on manpower, finances, and production cycle time (CT) of fabrication foundries (fabs). Virtual metrology (VM) presents an alternate approach to perform metrology inspection without incurring high costs using machine learning (ML) models. By leveraging historical equipment and process data, ML models can be calibrated to estimate the targeted metrology variables to estimate the quality of wafers, thereby performing virtual inspections on wafers. Recently, VM researchers have begun to introduce deep learning (DL) into VM research to examine its capability. Specifically, VM researchers experimented with a convolutional neural network (CNN). The targeted metrologies are those of plasma-based processes in both etching and chemical vapor deposition. The initial success has been reported by VM researchers. While various CNN-based VM models have been proposed for plasma-based fabrication processes, they have yet to be tested in the photolithography process. Motivated by the initial successes of CNN in plasma-based processes, this work is an initiative to experiment with CNN's performance in predicting the overlay errors of the photolithography process. Using data from a real fab, this study first establishes a baseline using the methodology of a prior study. The prediction results of the proposed CNN model are then compared with the baseline. The results showed that the CNN could further reduce prediction errors.