Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

Abstract: A semiconductor processing system includes a processing chamber configured to perform a semiconductor manufacturing process on each of a plurality of wafers. The processing chamber includes at least one consumable component, and a substrate handling module located proximate the processing chamber and in communication with the processing chamber via a wafer access port. The wafer handling module includes a wafer handling robot configured to transfer each of the wafers between to the substrate handling module and the processing chamber through the wafer access port, and an optical diagnostic system including an optical sensor configured to detect an optical signal from the at least one consumable component.

Abstract: A method of manufacturing semiconductor devices includes repeatedly performing a transfer operation which transfers each of a plurality of semiconductor wafers between a substrate handling module and a processing chamber through a wafer access port, the processing chamber including at least one consumable component. Using the processing chamber, a semiconductor manufacturing process is performed on each of the plurality of semiconductor wafers; and detecting an optical signal from the at least one consumable component during a time when the processing chamber is not performing the semiconductor manufacturing process on the wafers.

Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

Abstract: A method of measuring machine alignment offset of an optical machine having an alignment system, so that subsequent processing of substrates on set of optical machines can be performed in a machine-independent manner. The optical machine forms overlayed images of first and second patterns formed on either one or two reticles onto a substrate at respective first and second levels. The method of the invention includes forming a virtual zero-offset alignment pattern and a virtual zero-offset metrology pattern and imaging first and second metrology patterns on the substrate at the first and second levels, respectively. The second metrology pattern is aligned to the first metrology pattern using the zero-offset alignment pattern so that the exposures are performed in an overlayed manner. The first and second metrology patterns are based on the virtual zero-offset metrology pattern.

Abstract: A three-dimensional motion camera system comprises a light projector placed between two synchronous video cameras all focused on an object-of-interest. The light projector shines a sharp pattern of vertical lines (Ronchi ruling) on the object-of-interest that appear to be bent differently to each camera by virtue of the surface shape of the object-of-interest and the relative geometry of the cameras, light projector and object-of-interest Each video frame is captured in a computer memory and analyzed. Since the relative geometry is known and the system pre-calibrated, the unknown three-dimensional shape of the object-of-interest can be solved for by matching the intersections of the projected light lines with orthogonal epipolar lines corresponding to horizontal rows in the video camera frames. A surface reconstruction is made and displayed on a monitor screen. For 360.degree.