Applied Materials Israel Limited

Nova Measuring Instruments Ltd.

Nova Corporation

Boris Sherman

Boaz Brill

Igor Turovets

Ran Badanes

Yehonatan Hai Ofir

Boaz Cohen

Michele Dalla-Torre

Yehonatan Ridelman

Zion Hadad

Yotam Nissim Ben Shoshan

Yehuda Udy Danino

Noga Bullkich

There is provided a system and method of examination a semiconductor specimen. The method includes obtaining a runtime image of the specimen; processing the runtime image using a first machine learning (ML) model to extract a set of runtime features representative of a set of patches in the runtime image; and comparing the set of runtime features with a bank of reference features, giving rise to an anomaly map indicative of one or more defective patches in the runtime image. The bank of reference features is previously generated by obtaining a plurality of synthetic reference images generated by a second ML model based on a plurality of actual images; and processing the plurality of synthetic reference images by the first ML model to extract, for each synthetic reference image, a set of reference features representative thereof, giving rise to the bank of reference features.

Machine learning based defect examination for semiconductor specimens

There is provided a system and method for 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 during setup using a training set comprising one or more pairs of training images, each pair including a defective image and a corresponding defect-free image. The training comprises, for each pair, processing the defective image by the ML model to obtain a predicted image, and optimizing the ML model to minimize a difference between the predicted image and the defect-free image.

Defect examination on a semiconductor specimen

A method and system are presented for monitoring measurement of parameters of patterned structures based on a predetermined fitting model. The method comprises: (a) providing data indicative of measurements in at least one patterned structure; and (b) applying at least one selected verification mode to said data indicative of measurements, said at least one verification mode comprising: I) analyzing the data based on at least one predetermined factor and classifying the corresponding measurement result as acceptable or unacceptable, II) analyzing the data corresponding to the unacceptable measurement results and determining whether one or more of the measurements providing said unacceptable result are to be disregarded, or whether one or more parameters of the predetermined fitting model are to be modified.

Monitoring system and method for verifying measurements in patterned structures

A system receives, based on processing of an inspected frame of an inspected image generated by collecting signals indicative of a pattern on an article, at least one candidate defect location in the inspected frame. The system defines a candidate patch within the inspected frame. The candidate patch is associated with the candidate defect location. The system identifies at least one similar patch in the inspected frame using a predefined similarity criterion and determines whether a defect exists at the candidate defect location based on a comparison of at least a portion of the candidate patch with at least a corresponding portion of the at least one similar patch.

Apparatus and method for defect detection including patch-to-patch comparisons

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Rehovot, Israel

Boris Sherman