Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system configured to perform diagnostic functions for a deep learning model, comprising: one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise: a deep learning model configured for determining information from an image generated for a specimen by an imaging tool; and a diagnostic component configured for determining one or more causal portions of the image that, resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image. 2. The system of claim 1, wherein the information comprises a classification for a defect detected on the specimen. 3. The system of claim 1, wherein the information comprises features of the image extracted by the deep learning model. 4. The system of claim 1, wherein the information comprises a simulated image generated from the image. 5. The system of claim 1, wherein the information comprises one or more segmentation regions generated from the image. 6. The system of claim 1, wherein the information comprises a multi-dimensional output generated from the image. 7. The system of claim 1, wherein the deep learning model is a trained deep learning model. 8. The system of claim 1, wherein the deep learning model is further configured as a neural network. 9. The system of claim 1, wherein the one or more functions comprise altering one or more parameters of the deep learning model based on the determined one or more causal portions. 10. The system of claim 1, wherein the one or more functions comprise determining if the one or more causal portions that resulted in the information being determined are the correct one or more causal portions of the image. 11. The system of claim 1, wherein the one or more components further comprise a visualization component configured for displaying at least the image, the determined information, and the determined one or more causal portions to a user. 12. The system of claim 11, wherein the one or more components further comprise a user interface component configured to receive input from the user after said displaying, and wherein the one or more functions performed by the diagnostic component are determined based on the input from the user. 13. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by computing a local sensitivity. 14. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by causal back propagation. 15. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by causal back propagation performed using a deconvolution heatmap algorithm. 16. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by causal back propagation performed using a layer-wise relevance propagation. 17. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by causal back propagation performed using a deep lift algorithm. 18. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by global average pooling. 19. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by computing a path integral on gradients. 20. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by computing a partial dependence plot. 21. The system of claim 1, wherein the diagnostic component is further configured for determining the one or more causal portions by computing a partial dependence plot with path integral. 22. The system of claim 1, wherein the one or more functions comprise determining one or more characteristics of the one or more causal portions and determining, based on the one or more characteristics of the one or more causal portions, if additional images for the specimen should be collected from the imaging tool and used for additional training of the deep learning model. 23. The system of claim 1, wherein the one or more functions comprise determining one or more characteristics of the one or more causal portions and altering the image based on the one or more characteristics of the one or more causal portions to thereby generate an augmented image for input to the deep learning model. 24. The system of claim 1, wherein the one or more functions comprise determining one or more characteristics of the one or more causal portions and generating a data augmentation method for application to additional images input to the deep learning model. 25. The system of claim 1, wherein the one or more functions comprise identifying the one or more causal portions as one or more regions of interest in the image and tuning the deep learning model based on the one or more regions of interest. 26. The system of claim 1, wherein the one or more functions comprise identifying the one or more causal portions as one or more regions of interest in the image and training an additional deep learning model based on the one or more regions of interest. 27. The system of claim 1, wherein the imaging tool is configured as an inspection tool. 28. The system of claim 1, wherein the imaging tool is configured as a metrology tool. 29. The system of claim 1, wherein the imaging tool is configured as an electron beam based imaging tool. 30. The system of claim 1, wherein the imaging tool is configured as an optical based imaging tool. 31. The system of claim 1, wherein the specimen is a wafer. 32. The system of claim 1, wherein the specimen is a reticle. 33. A system configured to perform diagnostic functions for a deep learning model, comprising: an imaging tool configured for generating images of a specimen; one or more computer subsystems configured for acquiring the images; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise: a deep learning model configured for determining information from an image generated for the specimen by the imaging tool; and a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image. 34. A non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for performing diagnostic functions for a deep learning model, wherein the computer-implemented method comprises: determining information from an image generated for a specimen by an imaging tool by inputting the image to a deep learning model; determining one or more causal portions of the image that resulted in the information being determined by inputting the information to a diagnostic component; and performing one or more functions based on the determined one or more causal portions of the image with the diagnostic component, wherein the deep learning model and the diagnostic component are included in one or more components executed by the one or more computer systems. 35. A computer-implemented method for performing diagnostic functions for a deep learning model, comprising determining information from an image generated for a specimen by an imaging tool by inputting the image to a deep learning model; determining one or more causal portions of the image that resulted in the information being determined by inputting the information to a diagnostic component; and performing one or more functions based on the determined one or more causal portions of the image with the diagnostic component, wherein the deep learning model and the diagnostic component are included in one or more components executed by one or more computer systems.