公开(公告)号：US20240281956A1

公开(公告)日：2024-08-22

申请号：US18113032

申请日：2023-02-22

Applicant: Applied Materials Israel Ltd.

Inventor： Noam TAL ,  Boris LEVANT ,  Sergey SINITSA ,  Boaz STURLESI ,  Shay YOGEV ,  Assaf ARIEL ,  Lilach CHOONA ,  Shaul PRES

IPC: G06T7/00 ,  G01N21/88 ,  G06V10/44 ,  G06V10/70

CPC classification number: G06T7/0008 ,  G01N21/8851 ,  G06V10/44 ,  G06V10/70 ,  G06T2207/20081 ,  G06T2207/30148

Abstract: There is provided a system and method of examination of semiconductor specimens. The method includes generating a sequence of anomaly scores corresponding to a sequence of specimens sequentially fabricated and examined during a fabrication process, comprising, for each given specimen: obtaining an image of the given specimen acquired by an examination tool; using a machine learning (ML) model to process the image and obtaining an anomaly map indicative of pattern variation in the image; and deriving, based on the anomaly map, an anomaly score indicative of level of pattern variation presented in the given specimen, wherein the anomaly score is correlated with a defectivity score related to defect detection in a correlation relationship, and has higher detection sensitivity than the defectivity score; and analyzing the sequence of anomaly scores to monitor on-going process stability, thereby providing defect related prediction along the fabrication process based on the correlation relationship.

公开(公告)号：US20250086781A1

公开(公告)日：2025-03-13

申请号：US18244857

申请日：2023-09-11

Applicant: Applied Materials Israel Ltd.

Inventor： Boaz STURLESI ,  Noam TAL ,  Sergey SINITSA

IPC: G06T7/00 ,  G06T7/11

Abstract: There is provided a system and method of defect examination on a semiconductor specimen. The method comprises obtaining a runtime image of the semiconductor specimen; generating a reference image based on the runtime image using a machine learning (ML) model; and performing defect examination on the runtime image using the generated reference image. The ML model is previously trained alternately between two training modes using a training set: a stochastic mode where the ML model is configured to generate a predicted reference image with a stochastic pattern variation (PV) from a PV distribution, and a deterministic mode where the ML model is configured to generate a predicted reference image with a predetermined PV selected from the PV distribution, the PV distribution being learnt by the ML model based on PVs observed across the training set.