Abstract: A method for inspection of multiple features of patterned objects in the manufacture of electrical circuits, the method including performing defect detection on the patterned object, employing an optical defect detection machine (ODDM) and employing the ODDM to measure at least one of spatial coordinates and physical attributes of at least some of the multiple features.

Abstract: A method for inspection of multiple features of patterned objects in the manufacture of electrical circuits, the method including performing defect detection on the patterned object, employing an optical defect detection machine (ODDM) and employing the ODDM to measure at least one of spatial coordinates and physical attributes of at least some of the multiple features.

Abstract: A method for 3D imaging of samples using a machine learning algorithm is disclosed. The method uses multimodality focal stacks, which consist of a plurality of images acquired at two or more distances between the sample and a front focal plane, with at least one image acquired using the first modality and additional images acquired using additional modalities. The modalities may have different illumination angles and optionally different spectral distributions. The method may include receiving training images of samples, which include a plurality of training focal stacks, and ground truth 3D data (depth maps) for each training focal stack, and training a machine learning algorithm based on the training images and the ground truth data. The method subsequently receives product images of a sample, the product images including a focal stack from an optical assembly, and generates a 3D depth map.

Abstract: A method for inspection of multiple features of patterned objects in the manufacture of electrical circuits, the method including performing defect detection on the patterned object, employing an optical defect detection machine (ODDM) and employing the ODDM to measure at least one of spatial coordinates and physical attributes of at least some of the multiple features.

Abstract: A range differentiator useful for auto-focusing, the range differentiator including an image generator providing an image of a scene at various physical depths, a depth differentiator distinguishing portions of the image at depths below a predetermined threshold, irrespective of a shape of the portions, and providing a depth differentiated image and a focus distance ascertainer ascertaining a focus distance based on the depth differentiated image.

Abstract: A range differentiator useful for auto-focusing, the range differentiator including an image generator providing an image of a scene at various physical depths, a depth differentiator distinguishing portions of the image at depths below a predetermined threshold, irrespective of a shape of the portions, and providing a depth differentiated image and a focus distance ascertainer ascertaining a focus distance based on the depth differentiated image.