Interactive and iterative training of a classification algorithm for classifying anomalies in imaging datasets

Abstract

A method includes detecting a plurality of anomalies in an imaging dataset of a wafer. The wafer includes a plurality of semiconductor structures. The method also includes executing multiple iterations. At least some of the iterations include determining a current classification of the plurality of anomalies using a machine-learned classification algorithm and tiles of the imaging dataset associated with the plurality of anomalies. The current classification includes a current set of classes into which the anomalies of the plurality of anomalies are binned. The method further includes, based on at least one decision criterion, selecting at least one anomaly of the plurality of anomalies for a presentation to a user. In addition, the method includes, based on an annotation of the at least one anomaly provided by the user with respect to the current classification, re-training the classification algorithm.

Dependencies are inferred from the claim text.