There are provided system and method of segmentation a fabrication process (FP) image obtained in a fabrication of a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained to provide segmentation-related data, processing a fabrication process (FP) sample using the obtained trained DNN and, resulting from the processing, obtaining by the computer segments-related data characterizing the FP image to be segmented, the obtained segments-related data usable for automated examination of the semiconductor specimen. The DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image; FP sample comprises the FP image to be segmented.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of segmentation of a fabrication process (FP) image obtained in a fabrication of a semiconductor specimen, the method comprising: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide segmentation-related data, using the obtained trained DNN to process a fabrication process (FP) sample, wherein the DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image, and wherein the FP sample comprises the FP image to be segmented; and resulting from the processing, obtaining by the computer segments-related data characterizing the FP image, the obtained segments-related data usable for automated examination of the semiconductor specimen. 2. The method of claim 1, wherein the obtained segments-related data are informative of at least one of per-pixel values, per-pixel labels, CAD polygons, CAD models, regions of interest in the FP image, background of the FP image and foreground of the FP image. 3. The method of claim 1, wherein the FP image is selected from the group consisting of a high-resolution image of the specimen, a low-resolution image of the specimen and a design data-based image of the specimen. 4. The method of claim 1, wherein the obtained segments-related data are usable for automatic defect classification (ADC) to define the location of defect with regard to a background. 5. The method of claim 1, wherein the obtained segments-related data are usable for automatic defect review (ADR) to apply segment-specific detection thresholds. 6. The method of claim 1, wherein the obtained segments-related data are usable for detecting locations of potential defects. 7. The method of claim 1, wherein the obtained segments-related data are usable by another examination-related application using another DNN trained using a training set comprising ground truth data specific for the another application. 8. The method of claim 1, wherein the DNN is trained to provide segmentation-related data selected from the group consisting of a grayscale image in which different color value for each pixels represent different segments on the image, representation of edges of each segment and representation of vertices of each segment. 9. The method of claim 1, wherein each of the first training samples comprises at least one training image obtained by examination modality that is not available when obtaining the FP sample. 10. The method of claim 1, wherein the training set further comprises a plurality of augmented training samples obtained by augmenting at least part of the first training samples and augmented ground truth data associated with the augmented training samples. 11. The method of claim 9, wherein at least substantial part of augmented training samples is obtained by one or more augmentation techniques selected from the group consisting of adding noise, blurring, tone mapping and generating synthetic images. 12. A non-transitory computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform a method of classifying defects in a semiconductor specimen, the method comprising: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide segmentation-related data, using the obtained trained DNN to process a fabrication process (FP) sample, wherein the DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image, and wherein the FP sample comprises the FP image to be segmented; and resulting from the processing, obtaining by the computer segments-related data characterizing the FP image, the obtained segments-related data usable for automated examination of the semiconductor specimen. 13. The non-transitory computer readable medium of claim 12, wherein the obtained segments-related data are informative of at least one of per-pixel segmentation, per-pixel labels, CAD polygons, CAD models, regions of interest in the FP image, background and foreground of the FP image. 14. The non-transitory computer readable medium of claim 12, wherein the FP image is selected from the group consisting of a high-resolution image of the specimen, a low-resolution image of the specimen and a design data-based image of the specimen. 15. The non-transitory computer readable medium of claim 12, wherein the DNN is trained to provide segmentation-related data selected from the group consisting of a grayscale image in which different color value for each pixels represent different segments on the image, representation of edges of each segment and representation of vertices of each segment. 16. The non-transitory computer readable medium of claim 12, wherein the training set further comprises a plurality of augmented training samples obtained by augmenting at least part of the first training samples and augmented ground truth data associated with the augmented training samples and informative of segments-related data associated with the augmented training samples. 17. The non-transitory computer readable medium of claim 16, wherein at least substantial part of augmented training samples is obtained by one or more augmentation techniques selected from the group consisting of adding noise, blurring, tone mapping and generating synthetic images. 18. A system usable for examination of a semiconductor specimen, the system comprising a processing and memory block (PMB) operatively connected to an input interface and an output interface, wherein: the input interface is configured to receive one or more fabrication process (FP) samples each comprising a FP image to be segmented; the PMB is configured: to obtain a Deep Neural Network (DNN) trained to provide segmentation-related data and to process a fabrication process (FP) sample using the obtained trained DNN, wherein the DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image, and wherein the FP sample comprises the FP image to be segmented; and resulting from the processing, to obtain segments-related data characterizing the FP image, the obtained segments-related data usable for automated examination of the semiconductor specimen; and the output interface is configured to output the obtained segments-related data. 19. The system of claim 17, wherein the training set further comprises a plurality of augmented training samples obtained by augmenting at least part of the first training samples and augmented ground truth data associated with the augmented training samples and informative of segments-related data associated with the augmented training samples. 20. The system of claim 18, wherein at least substantial part of augmented training samples is obtained by one or more augmentation techniques selected from the group consisting of adding noise, blurring, tone mapping and generating synthetic images.