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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
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.
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公开(公告)号：US20240281958A1
公开(公告)日：2024-08-22
申请号：US18113030
申请日：2023-02-22
Applicant: Applied Materials Israel Ltd.
Inventor： Boris LEVANT , Noam TAL , Ran YACOBY , Lilach CHOONA , Shaul PRES , Jasmin Sonia LINSHIZ , Shay YOGEV , Assaf ARIEL
CPC classification number: G06T7/001 , G06N3/04 , G06N3/082 , H01J37/222 , H01J37/28 , H01L21/67288 , G06T2207/10061 , G06T2207/20081 , G06T2207/30148 , H01J2237/24578
Abstract: There is provided a system and method of examination of a semiconductor specimen. The method includes obtaining an e-beam image representative of a given layer of a given structure on the specimen in runtime, processing at least the e-beam image using a ML model, and obtaining yield related prediction with respect to the given structure prior to performing an electrical test. The ML model is previously trained using a training set comprising multiple stacks of e-beam images corresponding to multiple sites of the given structure on one or more training specimens, each stack of e-beam images representative of the at least given layer of a respective site; and test data acquired from an electrical test performed at the multiple sites and related to actual yield of the training specimens, the test data respectively correlated with the stacks of e-beam images and used as ground truth thereof.
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公开(公告)号：US20240105522A1
公开(公告)日：2024-03-28
申请号：US17947972
申请日：2022-09-19
Applicant: Applied Materials Israel Ltd.
Inventor： Tomer Haim PELED , Bar DUBOVSKI , Noam TAL , Bobin Mathew SKARIA , Boris LEVANT , Tal FRANK
CPC classification number: H01L22/12 , G06K9/6256 , G06N20/00
Abstract: There is provided a system and method for examining a semiconductor specimen. The method includes obtaining a runtime image of the specimen, and providing the runtime image as an input to an end-to-end (E2E) learning model to process, thereby obtaining, as an output of the E2E learning model, runtime measurement data specific for a metrology application. The E2E learning model is previously trained for the metrology application using a training set comprising a plurality of training images of the specimen and respective ground truth measurement data associated therewith, and one or more cost functions specifically configured to evaluate, for the plurality of training images and corresponding training measurement data outputted by the E2E learning model, one or more metrology benchmarks from a group comprising precision, correlation, and matching.
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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
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.
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