There are provided system and method of classifying defects in a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide classification-related attributes enabling minimal defect classification error, processing a fabrication process (FP) sample using the obtained trained DNN; and, resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification, in accordance with the obtained classification-related attributes, of the at least one defect presented in the FP image. The DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples; the FP sample comprises a FP image presenting at least one defect to be classified.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. 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 classification-related attributes enabling minimal defect classification error, using the obtained trained DNN to process a fabrication process (FP) sample, wherein the DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples, and wherein the FP sample comprises a FP image presenting at least one defect to be classified; and resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification, in accordance with the obtained classification-related attributes, of the at least one defect presented in the FP image. 2. The method of claim 1, wherein the training image is obtained by an examination modality selected from the group consisting of: optical inspection; multi-perspective optical inspection, low-resolution inspection by electronic microscope, high-resolution inspection by electronic microscope, image generation based on design data, image generation by altering a captured image. 3. The method of claim 1, wherein each of the first training samples comprises at least one image obtained by an examination modality that is not available for obtaining the FP sample. 4. The method of claim 1, wherein each of the first training samples further comprises one or more additional training images corresponding to respective training images representing at least one defect and of a type selected from the group consisting of reference die image, CAD-based image, height map, defect mask and multi-perspective images, different first training samples comprising the same type and number of additional training; and wherein the FP sample further comprises one or more additional FP images in correspondence with the one or more additional training images in the first training samples. 5. The method of claim 1, wherein each given FT sample comprises, merely, a single FP image presenting at least one defect to be classified, and wherein the classification-related attributes are obtained by processing the single FP image. 6. 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 and informative of classes and/or of class distribution of defects presented in the augmented training samples. 7. The method of claim 6, wherein at least substantial part of augmented training samples is obtained by one or more augmenting techniques selected from the group consisting of geometrical warping, implanting a new defect in an image, amplifying a defectiveness of a pre-existing defect in an image, removing a pre-existing defect from an image and disguising a defect in an image. 8. The method of claim 1 further comprising exporting the obtained classification-related attributes to an external classification system to generate classification of the at least one defect presented in the FP image. 9. The method of claim 1 further comprising: obtaining, by the computer, engineered attributes related to the at least one defect to be classified; processing by the computer the obtained classification-related attributes together with the engineered attributes to generate intermediate classification results; and exporting the intermediate classification results and the engineered attributes to an external classification system to generate classification of the at least one defect presented in the FP image. 10. The method of claim 1 further comprising: a. obtaining by the computer engineered attributes related to the at least one defect to be classified; b. processing by the computer the obtained classification-related attributes together with the engineered attributes to yield classification of the at least one defect presented in the FP image. 11. 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 classification-related attributes enabling minimal defect classification error, processing a fabrication process (FP) sample using the obtained trained DNN, wherein the DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples, and wherein the FP sample comprises a FP image presenting at least one defect to be classified; and resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification of the at least one defect in accordance with the obtained classification-related attributes. 12. The non-transitory computer readable medium of claim 11, wherein the training image is obtained by an examination modality selected from the group consisting of: optical inspection; multi-perspective optical inspection, low-resolution inspection by electronic microscope, high-resolution inspection by electronic microscope, image generation based on design data, image generation by altering a captured image. 13. The non-transitory computer readable medium of claim 11, wherein each of the first training samples comprises at least one image obtained by an examination modality that is not available for obtaining the FP sample. 14. The non-transitory computer readable medium of claim 11, wherein each of the first training samples further comprises one or more additional images corresponding to respective training images representing at least one defect and of a type selected from the group consisting of reference die image, CAD-based image, height map, defect mask and multi-perspective images, the one or more additional images in different first training samples being of the same type; and wherein the FP sample further comprises one or more additional images selected, in correspondence with the types of the one or more additional images in the first training samples, from the group consisting of reference die image, CAD-based image, height map, defect mask and FP multi-perspective images. 15. The non-transitory computer readable medium of claim 11, wherein each given FT sample comprises, merely, a single FP image presenting at least one defect to be classified, and wherein the classification-related attributes are obtained by processing the single FP image. 16. The non-transitory computer readable medium of claim 11, 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 classes and/or of class distribution of defects presented in the augmented training samples. 17. A system usable for examination of a semiconductor specimen, the system comprising a processing and memory block (PMB) operatively connected to an input interface, wherein: the input interface is configured to receive one or more fabrication process (FP) samples each comprising a FP image presenting at least one defect to be classified; the PMB is configured to obtain a Deep Neural Network (DNN) trained to provide classification-related attributes enabling minimal defect classification error and to process a fabrication process (FP) sample using the obtained trained DNN, wherein the DNN is trained using a classification training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprising a training image presenting at least one defect and the ground truth data is informative of classes and/or class distribution of defects presented in the respective first training samples, and wherein the; and resulting from the processing, to obtain classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification of the at least one defect in accordance with the obtained classification-related attributes. 18. The system of claim 17 further comprising an output interface configured to export the obtained classification-related attributes to an external classification system to generate classification of the at least one defect presented in the FP image. 19. The system of claim 17, wherein the PMB is further configured to obtain engineered attributes related to the at least one defect to be classified and to process the obtained classification-related attributes together with the engineered attributes to generate intermediate classification results; and wherein the system further comprises an output interface configured to export the intermediate classification results and the engineered attributes to an external classification system to generate classification of the at least one defect presented in the FP on image. 20. The system of claim 17, wherein the PMB is further configured to obtain engineered attributes related to the at least one defect to be classified and to process the obtained classification-related attributes together with the engineered attributes to yield classification of the at least one defect presented in the FP image.