Abstract: The presently disclosed subject matter includes a computer system and a computer-implemented method of generating synthetic examination output images, including synthetic fault images and synthetic fault-free images. The synthetic fault images comprise artificially generated 3D defects. The proposed technique enables fast, accurate, and efficient generation of a large and diverse collection of synthetic fault images and fault-free images, which can be implemented in runtime, as part of the examination process of semiconductor specimens.

Abstract: The presently disclosed subject matter includes a computer system and a computer-implemented method of generating synthetic examination output images, including synthetic fault images and synthetic fault-free images. The synthetic images comprise artificially generated 3D defects. A machine learning model is trained for transforming examination output images to height maps. The height maps are modified to include certain 3D features, and a second machine learning model is used for transforming the height maps to modified examination output images to exhibit the 3D features.

Abstract: There is provided a system and method of runtime defect detection in a semiconductor specimen. The method includes obtaining a runtime image of the specimen; and processing, by a detection network, the runtime image to obtain a defect map indicating probabilities of defect distribution thereof. The detection network is previously trained unsupervised in a training phase, comprising, for a training image: obtaining a reference image of the training image; processing, by a detection network to be trained, the training image to generate a predicted defect map thereof; and optimizing the detection network to be trained using a loss function constructed based on the predicted defect map, and a difference image between the training image and the reference image.

Abstract: There is provided a system and method of examining a semiconductor specimen. The method includes obtaining an input image of the semiconductor specimen; processing the input image using a first machine learning (ML) model, to obtain a synthetic image corresponding to the input image, where the synthetic image is reconstructed to resemble a target image pertaining to the specific application; processing, by a second ML model, the synthetic image and one of the input image or the target image of the synthetic image, to obtain a defect map indicative of defect distribution in the input image or the target image with respect to the synthetic image; and verifying quality of the synthetic image based on the defect map. The first ML model is previously trained for image reconstruction for a specific application, and the second ML model is previously trained for defect detection.

Abstract: 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.

Abstract: 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.

Abstract: There is provided a system and method of semiconductor specimen examination. The method includes obtaining a plurality of images of a semiconductor specimen acquired by an examination tool; processing the plurality of images using a first machine learning (ML) model for defect detection, thereby obtaining, from the plurality of images, a set of images labeled with detected defects, wherein the first ML model is previously trained using a first training set comprising a subset of synthetic defective images each containing one or more synthetic defects, and a subset of nominal images; and training a second ML model using a second training set comprising at least part of the set of images labeled with detected defects, wherein the second ML model, upon being trained, is usable for defect detection with improved detection performance with respect to the first ML model.

Abstract: 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.

Abstract: 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.

Abstract: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

Abstract: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.