There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of examination of a semiconductor specimen, the method comprising: upon obtaining by a computer a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining by the computer examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. 2. The method of claim 1, wherein the training set comprises a plurality of first training samples and a plurality of augmented training samples obtained by augmenting at least part of the first training samples. 3. The method of claim 2, wherein the training set comprises ground truth data associated with the first training samples and augmented ground truth data associated with the augmented training samples. 4. The method of claim 2, wherein at least substantial part of augmented training samples is obtained by one or more augmenting techniques preferable to the given application. 5. The method of claim 1, wherein the training set comprises a plurality of first training samples, each first training sample comprises at least one image 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. 6. The method of claim 1, wherein the one or more FP images constitute a FP sample and wherein the training set comprises a plurality of first training samples each comprising images obtained from the same modalities as the one or more FP images. 7. The method of claim 6, wherein each first training sample further comprises at least one image obtained by an examination modality other than one or more examination modalities used for obtaining the one or more FP images. 8. The method of claim 1, wherein the given examination-related application is related to a certain production layer, wherein the training set comprises ground truth data specific for said certain production layer and wherein the examination-related data is specific for said certain production layer. 9. The method of claim 1, wherein the given examination-related application is related to a certain virtual layer consisting of one or more production layers with a similar nature, wherein the training set comprises ground truth data specific for said certain virtual layer and wherein the examination-related data is specific for said certain virtual layer. 10. The method of claim 1, wherein the examination comprises at least a first examination-related application and a second examination-related application, the method further comprising using for the first application a DNN trained using a training set comprising ground truth data specific for the first application and using for the second application a DNN trained using a training set comprising ground truth data specific for the second application. 11. The method of claim 3, wherein the given examination-related application is classifying at least one defect presented by the at least one FP image, and wherein the ground truth data is informative of classes and/or of class distribution of defects presented in the first training samples and augmented ground truth data is informative of classes and/or of class distribution of defects presented in the augmented training samples. 12. The method of claim 2, wherein the given examination-related application is classifying at least one defect presented by the at least one FP image, and wherein augmenting at least part of the first training samples is provided by at least one technique selected from the group consisting of geometrical warping, planting 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. 13. The method of claim 1, wherein the given examination-related application is segmenting the at least one FP image which is selected from the group comprising a high-resolution image of the specimen, a low-resolution image of the specimen and a design data-based image of the specimen; and wherein the examination-related data is informative of per-pixel segmentation-related values of the at least one FP image. 14. The method of claim 1, wherein the given examination-related application is detecting defects in the specimen and wherein the examination-related data is informative of true defects presented in the at least one FP image. 15. The method of claim 1, wherein the given examination-related application is registration between at least two FP images, wherein the training set comprises a plurality of training samples each comprising at least a pair of images registerable one with regard to another, and wherein the examination-related data is informative of registration-related data with regard to said at least two FP images. 16. The method of claim 1, wherein the given examination-related application is regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, wherein the training set comprises a plurality of training samples with images obtained by first examination modalities, each said training sample associated with ground truth data comprising one or more corresponding images obtained by one or more another examination modalities. 17. A non-transitory computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform a method of examination of a semiconductor specimen, the method comprising: upon obtaining by the computer a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining by the computer examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. 18. The non-transitory computer readable medium of claim 17, wherein the training set comprises a plurality of first training samples, a plurality of augmented training samples obtained by augmenting at least part of the first training samples, ground truth data associated with the first training samples and augmented ground truth data associated with the augmented training samples. 19. The non-transitory computer readable medium of claim 17, wherein the given examination-related application is selected from the group consisting of classifying at least one defect presented by the at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, and regression application enabling reconstructing the at least one FP image in correspondence with different examination modality. 20. 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) images; the PMB is configured to obtain a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process and to process together the one or more FP images using the trained DNN to obtain examination-related data specific for the given application and characterizing at least one of the processed one or more FP images, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; the output interface is configured to output the obtained examination-related data.