Abstract:
A method for detecting crystal defects includes scanning a first FOV on a first sample using a charged particle beam with a plurality of different tilt angles. BSE emitted from the first sample are detected and a first image of the first FOV is created. A first area within the first image is identified where signals from the BSE are lower than other areas of the first image. A second FOV on a second sample is scanned using approximately the same tilt angles or deflections as those used to scan the first area. The BSE emitted from the second sample are detected and a second image of the second FOV is created. Crystal defects within the second sample are identified by identifying areas within the second image where signals from the BSE are different than other areas of the second image.
Abstract:
A method for classifying defects of a wafer, the method is executed by a computerized system, the method may include obtaining defect candidate information about a group of defect candidates, wherein the defect candidate information comprises values of attributes per each defect candidate of the group; selecting, by a processor of the computerized system, a selected sub-group of defect candidates in response to values of attributes of defect candidates that belong to at least the selected sub-group; classifying defect candidates of the selected sub-group to provide selected sub-group classification results; repeating, until fulfilling a stop condition: selecting an additional selected sub-group of defect candidates in response to (a) values of attributes of defect candidates that belong to at least the additional selected sub-group; and (b) classification results obtained from classifying at least one other selected sub-group; and classifying defect candidates of the additional selected sub-group to provide additional selected sub-group classification results.
Abstract:
A method for detecting crystal defects includes scanning a first FOV on a first sample using a charged particle beam with a plurality of different tilt angles. BSE emitted from the first sample are detected and a first image of the first FOV is created. A first area within the first image is identified where signals from the BSE are lower than other areas of the first image. A second FOV on a second sample is scanned using approximately the same tilt angles or deflections as those used to scan the first area. The BSE emitted from the second sample are detected and a second image of the second FOV is created. Crystal defects within the second sample are identified by identifying areas within the second image where signals from the BSE are different than other areas of the second image.
Abstract:
A computerized system that may include a recipe module and a yield diagnostics module. The yield diagnostics module may be configured to generate evaluation results that are indicative of an outcome of an evaluation process of at least one manufacturing stage of at least one electrical circuit. The evaluation results differ from end of line (EOL) results. The recipe module may be configured to receive EOL results relating to the at least one electrical circuit, to receive the evaluation results relating to the at least one electrical circuit; to correlate the evaluation results and the EOL results to provide correlation results; and respond to the correlation results. The responding to the correlation results may include determining whether to alter a recipe in response to the correlation results and altering the recipe if it is determined to alter the recipe.
Abstract:
A computerized system that may include a recipe module and a yield diagnostics module. The yield diagnostics module may be configured to generate evaluation results that are indicative of an outcome of an evaluation process of at least one manufacturing stage of at least one electrical circuit. The evaluation results differ from end of line (EOL) results. The recipe module may be configured to receive EOL results relating to the at least one electrical circuit, to receive the evaluation results relating to the at least one electrical circuit; to correlate the evaluation results and the EOL results to provide correlation results; and respond to the correlation results. The responding to the correlation results may include determining whether to alter a recipe in response to the correlation results and altering the recipe if it is determined to alter the recipe.
Abstract:
A system for electrically testing an object, the system may include a scanning electron microscope that comprises a column; and nano-probe modules that are mechanically connected to the column; wherein the column is configured to illuminate areas of the object, with a beam of charged particles; wherein nano-probes of the nano-probe modules are configured to electrically contact elements of the object, during electrical tests of the object, wherein the elements of the object are located within the areas of the object.
Abstract:
A method for classifying defects of a wafer, the method is executed by a computerized system, the method may include obtaining defect candidate information about a group of defect candidates, wherein the defect candidate information comprises values of attributes per each defect candidate of the group; selecting, by a processor of the computerized system, a selected sub-group of defect candidates in response to values of attributes of defect candidates that belong to at least the selected sub-group; classifying defect candidates of the selected sub-group to provide selected sub-group classification results; repeating, until fulfilling a stop condition: selecting an additional selected sub-group of defect candidates in response to (a) values of attributes of defect candidates that belong to at least the additional selected sub-group; and (b) classification results obtained from classifying at least one other selected sub-group; and classifying defect candidates of the additional selected sub-group to provide additional selected sub-group classification results.
Abstract:
A method for classifying defects of a wafer, the method is executed by a computerized system, the method may include obtaining defect candidate information about a group of defect candidates, wherein the defect candidate information comprises values of attributes per each defect candidate of the group; selecting, by a processor of the computerized system, a selected sub-group of defect candidates in response to values of attributes of defect candidates that belong to at least the selected sub-group; classifying defect candidates of the selected sub-group to provide selected sub-group classification results; repeating, until fulfilling a stop condition: selecting an additional selected sub-group of defect candidates in response to (a) values of attributes of defect candidates that belong to at least the additional selected sub-group; and (b) classification results obtained from classifying at least one other selected sub-group; and classifying defect candidates of the additional selected sub-group to provide additional selected sub-group classification results.