Maximizing Efficiency of Inspection tools using a novel high capacity unsupervised machine learning technique

In this paper, we are proposing techniques to maximize efficiency of inspection tools with the help of machine learning algorithm. The proposed techniques optimized the selection of inspection locations to measure as many different patterns as possible. It can cover more than 99% of patterns on wafer with a very limited measurement budget ~ 100 measurement locations. This can be helpful in many applications such as model calibration, Hot spot detection, measure patterns fidelity on wafer and much more.