Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: An optical system includes an oriented polymeric multilayer optical film having a first reflection band, and a light source configured to produce light in an output band and/or a sensor configured to receive light in an input band. The light source and/or sensor is in optical communication with the oriented polymeric multilayer optical film. In some cases, the first reflection band overlaps the input and/or output band at normal incidence, but not at an oblique incidence angle. In some cases, the first reflection band overlaps the input and/or output band at an oblique incidence angle, but not at normal incidence. The optical system can further include a non-birefringent optical filter having a first blocking band where the first blocking band overlaps the first reflection band for at least one of normal incidence or an oblique incidence angle.

Abstract: An optical stack including an oriented polymeric multilayer optical film and a non-birefringent optical filter is described. The oriented polymeric multilayer optical film has a first reflection band with a first band edge and the non-birefringent optical filter has a first blocking band. In some cases, the first blocking band contains the first band edge and the first blocking band provides a reduction in variation of a band edge of an overall blocking band of the optical stack.

Abstract: An optical stack including an oriented polymeric multilayer optical film and a non-birefringent optical filter is described. The oriented polymeric multilayer optical film has a first reflection band with a first band edge and the non-birefringent optical filter has a first blocking band. In some cases, the first blocking band contains the first band edge and the first blocking band provides a reduction in variation of a band edge of an overall blocking band of the optical stack.

Abstract: A system and method for evaluating wafer test probe cards under real-world wafer test cell condition integrates wafer test cell components into the probe card inspection and analysis process. Disclosed embodiments may utilize existing and/or modified wafer test cell components such as, a head plate, a test head, a signal delivery system, and a manipulator to emulate wafer test cell dynamics during the probe card inspection and analysis process.

Abstract: A system and method for evaluating wafer test probe cards under real-world wafer test cell condition integrates wafer test cell components into the probe card inspection and analysis process. Disclosed embodiments may utilize existing and/or modified wafer test cell components such as, a head plate, a test head, a signal delivery system, and a manipulator to emulate wafer test cell dynamics during the probe card inspection and analysis process.

Abstract: A system and method for evaluating wafer test probe cards under real-world wafer test cell condition integrates wafer test cell components into the probe card inspection and analysis process. Disclosed embodiments may utilize existing and/or modified wafer test cell components such as, a head plate, a test head, a signal delivery system, and a manipulator to emulate wafer test cell dynamics during the probe card inspection and analysis process.

Abstract: A method of optimizing an optical inspection and fabrication process is herein disclosed. Images, preferably color digital images, of an object are obtained and multiple filter space representations of these images are created. Each of the representations and the channels or data that define them are analyzed separately or in combination with one another to determine which representations, combination of representations, channels, combinations of channels, data or combinations of data provide the most optimal data for analysis by optical inspection algorithms. The process may be automated in terms of the creation of image representations and/or single or multivariate analysis.

Abstract: A system and method for evaluating wafer test probe cards under real-world wafer test cell condition integrates wafer test cell components into the probe card inspection and analysis process. Disclosed embodiments may utilize existing and/or modified wafer test cell components such as, a head plate, a test head, a signal delivery system, and a manipulator to emulate wafer test cell dynamics during the probe card inspection and analysis process.