Abstract: A method and apparatus for analyzing an input electron microscope image of a first area on a first wafer are disclosed. The method comprises obtaining a plurality of mode images from the input electron microscope image corresponding to a plurality of interpretable modes. The method further comprises evaluating the plurality of mode images, and determining, based on evaluation results, contributions from the plurality of interpretable modes to the input electron microscope image. The method also comprises predicting one or more characteristics in the first area on the first wafer based on the determined contributions. In some embodiments, a method and apparatus for performing an automatic root cause analysis based on an input electron microscope image of a wafer are also disclosed.

Abstract: A method and apparatus for identifying locations to be inspected on a substrate is disclosed. A defect location prediction model is trained using a training dataset associated with other substrates to generate a prediction of defect or non-defect and a confidence score associated with the prediction for each of the locations based on process-related data associated with the substrates. Those of the locations determined by the defect location prediction model as having confidences scores satisfying a confidence threshold are added to a set of locations to be inspected by an inspection system. After the set of locations are inspected, the inspection results data is obtained, and the defect location prediction model is incrementally trained by using the inspection results data and process-related data for the set of locations as training data.

Abstract: A method for determining a probabilistic model configured to predict a characteristic (e.g., defects, CD, etc.) of a pattern of a substrate subjected to a patterning process. The method includes obtaining a spatial map of a distribution of a residue corresponding to a characteristic of the pattern on the substrate, determining a zone of the spatial map based on a variation of the distribution of the residue within the spatial map, and determining the probabilistic model based on the zone and the distribution of the residue values or the values of the characteristic of the pattern on the substrate within the zone.

Abstract: Objective lens alignment of a scanning electron microscope review tool with fewer image acquisitions can be obtained using the disclosed techniques and systems. Two different X-Y voltage pairs for the scanning electron microscope can be determined based on images. A second image based on the first X-Y voltage pair can be used to determine a second X-Y voltage pair. The X-Y voltage pairs can be applied at the Q4 lens or other optical components of the scanning electron microscope.

Abstract: A method for determining a probabilistic model configured to predict a characteristic (e.g., defects, CD, etc.) of a pattern of a substrate subjected to a patterning process. The method includes obtaining a spatial map of a distribution of a residue corresponding to a characteristic of the pattern on the substrate, determining a zone of the spatial map based on a variation of the distribution of the residue within the spatial map, and determining the probabilistic model based on the zone and the distribution of the residue values or the values of the characteristic of the pattern on the substrate within the zone.

Abstract: Objective lens alignment of a scanning electron microscope review tool with fewer image acquisitions can be obtained using the disclosed techniques and systems. Two different X-Y voltage pairs for the scanning electron microscope can be determined based on images. A second image based on the first X-Y voltage pair can be used to determine a second X-Y voltage pair. The X-Y voltage pairs can be applied at the Q4 lens or other optical components of the scanning electron microscope.

Abstract: One embodiment relates to a method for automated review of defects detected in a defective die on the target substrate. The method includes: performing an automated review of the defects using an secondary electron microscope (SEM) so as to obtain electron-beam images of the defects; performing an automated classification of the defects into types based on morphology of the defects as determined from the electron-beam images; selecting defects of a specific type for automated energy-dispersive x-ray (EDX) review; and performing the automated EDX review on the defects of the specific type. In addition, automated techniques are disclosed for obtaining an accurate reference so as to improve the usefulness of the EDX results. Furthermore, an automated method of classifying the defects based on the EDX results is disclosed which provides a final pareto that combines both morphological and elemental information. Other embodiments, aspects and features are also disclosed.

Abstract: One embodiment relates to a method for automated review of defects detected in a defective die on the target substrate. The method includes: performing an automated review of the defects using an secondary electron microscope (SEM) so as to obtain electron-beam images of the defects; performing an automated classification of the defects into types based on morphology of the defects as determined from the electron-beam images; selecting defects of a specific type for automated energy-dispersive x-ray (EDX) review; and performing the automated EDX review on the defects of the specific type. In addition, automated techniques are disclosed for obtaining an accurate reference so as to improve the usefulness of the EDX results. Furthermore, an automated method of classifying the defects based on the EDX results is disclosed which provides a final pareto that combines both morphological and elemental information. Other embodiments, aspects and features are also disclosed.

Abstract: The invention discloses methods for making photonic bandgap structures and photonic bandgap structures made by those processes. In one embodiment, the photonic bandgap structure is flexible. In another photonic bandgap structure, the structure has a graded, periodic grating. One embodiment of a method according to the present invention comprises the steps of preparing a pre-polymer mixture, positioning that mixture between two slides, exposing the mixture to electromagnetic radiation, curing the mixture, and discarding at least one of the slides. In another embodiment of the method, the pre-polymer mixture is exposed to the electromagnetic radiation through a prism. In one embodiment of the method, the pre-polymer mixture is exposed to the electromagnetic radiation through a lens. In one embodiment of the invention, the photonic bandgap structure is used as a filter in a multispectral imaging device comprising a imaging device, the filter, a processor, and an electronic image storage device.