KLA Corporation
Deep learning networks for nuisance filtering

Abstract:

Methods and systems for detecting defects on a specimen are provided. One system includes a first deep learning (DL) network configured for filtering nuisances from defect candidates detected on a specimen. Output of the first DL network includes a first subset of the defect candidates not filtered as the nuisances. The system also includes a second DL network configured for filtering nuisances from the first subset of the defect candidates. Computer subsystem(s) input high resolution images acquired for the first subset of the defect candidates into the second DL network. Output of the second DL network includes a final subset of the defect candidates not filtered as the nuisances. The computer subsystem(s) designate the defect candidates in the final subset as defects on the specimen and generate results for the defects.