Advanced pattern defect detection and analysis with artificial intelligence

Abstract

As semiconductor process technology needs to be advanced, the difficulty of patterning increases and unexpected pattern defects occur. In fact, it is very important to quickly identify and resolve these pattern defects in advance, but there is a limit to measuring all of these pattern defects that occur in wafers. In particular, in order to overcome metrological limitations, it may be best to extract actual potential pattern defects by classifying various types of patterns that actually exist. We measured how to classify weak patterns by sampling them based on previously known weak pattern group libraries, simulation-based weak patterns, and unique patterns. Through this method, engineers can subjectively judge how to find weak patterns, and it can be difficult because there is a possibility that the probability of weak patterns is low depending on the limited measurement capacity. In this paper, unsupervised learning is used to cluster and classify by pattern type based on the various features of pattern. Then, based on reliable wafer data for various classified pattern types, the degree of vulnerability to defect was quantified for each classified cluster to give a ranking for extracting a weak pattern group for each cluster, and the weak pattern was extracted based on this to confirm a high weak pattern detection rate. In addition, it provides effective solutions to extract weak patterns from various databases (DBs) and specifically to give reliability to visualization methods.