Abstract: Feature extraction and classification is used for process window monitoring. A classifier, based on combinations of metrics of masked die images and including a set of significant combinations of one or more segment masks, metrics, and wafer images, is capable of detecting a process non-compliance. A process status can be determined using a classifier based on calculated metrics. The classifier may learn from nominal data.

Abstract: Shadow-grams are used for edge inspection and metrology of a stacked wafer. The system includes a light source that directs collimated light at an edge of the stacked wafer, a detector opposite the light source, and a controller connected to the detector. The stacked wafer can rotate with respect to the light source. The controller analyzes a shadow-gram image of the edge of the stacked wafer. Measurements of a silhouette of the stacked wafer in the shadow-gram image are compared to predetermined measurements. Multiple shadow-gram images at different points along the edge of the stacked wafer can be aggregated and analyzed.

Abstract: Feature extraction and classification is used for process window monitoring. A classifier, based on combinations of metrics of masked die images and including a set of significant combinations of one or more segment masks, metrics, and wafer images, is capable of detecting a process non-compliance. A process status can be determined using a classifier based on calculated metrics. The classifier may learn from nominal data.

Abstract: Shadow-grams are used for edge inspection and metrology of a stacked wafer. The system includes a light source that directs collimated light at an edge of the stacked wafer, a detector opposite the light source, and a controller connected to the detector. The stacked wafer can rotate with respect to the light source. The controller analyzes a shadow-gram image of the edge of the stacked wafer. Measurements of a silhouette of the stacked wafer in the shadow-gram image are compared to predetermined measurements. Multiple shadow-gram images at different points along the edge of the stacked wafer can be aggregated and analyzed.

Abstract: Method for detecting textural defects in an image. The image, which may have an irregular visual texture, may be received. The image may be decomposed into a plurality of subbands. The image may be portioned into a plurality of partitions. A plurality of grey-level co-occurrence matrices (GLCMs) may be determined for each partition. A plurality of second-order statistical attributes may be extracted for each GLCM. A feature vector may be constructed for each partition, where the feature vector includes the second order statistical attributes for each GLCM for the partition. Each partition may be classified based on the feature vector for the respective partition. Classification of the partitions may utilize a one-class support vector machine, and may determine if a defect is present in the image.

Abstract: Method for detecting textural defects in an image. The image, which may have an irregular visual texture, may be received. The image may be decomposed into a plurality of subbands. The image may be portioned into a plurality of partitions. A plurality of grey-level co-occurrence matrices (GLCMs) may be determined for each partition. A plurality of second-order statistical attributes may be extracted for each GLCM. A feature vector may be constructed for each partition, where the feature vector includes the second order statistical attributes for each GLCM for the partition. Each partition may be classified based on the feature vector for the respective partition. Classification of the partitions may utilize a one-class support vector machine, and may determine if a defect is present in the image.