Towards Big Data Visualization for Monitoring and Diagnostics of High Volume Semiconductor Manufacturing

Abstract

In semiconductor manufacturing, continuous on-line monitoring prevents production stop and yield loss. The challenges towards this accomplishment are: 1) the complexity of lithography machines which are composed of hundreds of mechanical and optical components, 2) the high rate and volume data acquisition from different lithography and metrology machines, and 3) the scarcity of performance measurements due to their cost. This paper addresses these challenges by 1) visualizing and ranking the most relevant factors to a performance metric, 2) organizing efficiently Big Data from different sources and 3) predicting the performance with machine learning when measurements are lacking. Even though this project targets semiconductor manufacturing, its methodology is applicable to any case of monitoring complex systems, with many potentially interesting features, and imbalanced datasets.