Abstract: A fabricated device having consistent modulation between target and reference components is provided. The fabricated device includes a target component having a first modulation. The fabricated device further includes at least two reference components for the target component including a first reference component and a second reference component, where the first reference component and the second reference component each have the first modulation. Further, a system, method, and computer program product are provided for detecting defects in a fabricated target component using consistent modulation for the target and reference components.

Abstract: A system, method, and computer program product are provided for correcting a difference image generated from a comparison of target and reference dies. In use, an intra-die inspection of a target die image is performed to generate, for each pattern of interest, a first representative image. An intra-die inspection of a reference die image is performed to generate, for each of the patterns of interest, a second representative image. Further, the target die image and the reference die image are compared to generate at least one difference image, and the at least one difference image is corrected using each of the generated first representative images and each of the generated second representative images. Detection is then performed using the corrected difference image.

Abstract: Methods and systems for setting up a wafer inspection recipe are provided. Inspection results produced by complete wafer inspection recipe candidates, each of which includes one or more optical mode candidates with at least one set of defect detection parameters, are compared to determine which of the complete wafer inspection recipe candidates is the best for use as the wafer inspection recipe. The method does not involve making any decisions regarding performance of the complete wafer inspection recipe candidates until after the inspection results have been compared. In other words, the method does not involve selecting optical mode(s) that will be used in the wafer inspection recipe followed by selecting the defect detection parameters for the selected optical mode(s). In this manner, a greater number of optical mode and defect detection parameters can be considered in an efficient manner to determine the best wafer inspection recipe for any given wafer.

Abstract: Methods and systems for setting up a classifier for defects detected on a wafer are provided. One method includes generating a template for a defect classifier for defects detected on a wafer and applying the template to a training data set. The training data set includes information for defects detected on the wafer or another wafer. The method also includes determining one or more parameters for the defect classifier based on results of the applying step.

Abstract: Methods and systems for classifying defects detected on a specimen with an adaptive automatic defect classifier are provided. One method includes creating a defect classifier based on classifications received from a user for different groups of defects in first lot results and a training set of defects that includes all the defects in the first lot results. The first and additional lot results are combined to create cumulative lot results. Defects in the cumulative lot results are classified with the created defect classifier. If any of the defects are classified with a confidence below a threshold, the defect classifier is modified based on a modified training set that includes the low confidence classified defects and classifications for these defects received from a user. The modified defect classifier is then used to classify defects in additional cumulative lot results.

Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

Abstract: Methods and systems for generating a defect sample for a wafer are provided. One method includes separating defects detected on a wafer into bins having diversity in values of a first set of one or more first attributes of the defects. The method also includes selecting, independently from one or more of the bins, defects within the bins based on diversity in a second set of one or more second attributes of the defects. The selected defects are then used to create a defect sample for the wafer. In this manner, defects having diverse values of multiple attributes can be easily selected.

Abstract: A method for production line monitoring during semiconductor device fabrication includes acquiring a plurality of inspection results from a plurality of reference samples with an inspection sub-system. The method includes storing the acquired inspection results and geometric pattern codes for each of the reference samples in a database. The method includes acquiring an additional inspection result from an additional sample, where the additional inspection result includes an additional set of geometric pattern codes for identifying each defect identified within the additional inspection result from the additional sample. The method also includes correlating the set of geometric pattern codes of the additional sample with the geometric pattern codes from the reference set of samples to identify at least one of one or more new patterns or one or more patterns displaying a frequency of occurrence above a selected threshold.

Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

Abstract: Methods and systems for determining a process window for a process performed on a specimen are provided. In general, the embodiments preferentially sample locations in an instance of at least a portion of a device formed on a specimen at a value of a parameter of a process performed on the specimen that is closest to an edge of a determined process window for the process. If defects are detected at the sampled locations, then the sampling may be performed again but for a different instance of the device formed at a value of the parameter that is closer to nominal than the previously used value. When no defects are detected at the sampled locations, then the sampling may be ended, and the determined process window may be modified based on the value of the parameter corresponding to the instance of the device in which no defects were detected.

Abstract: Methods and systems for generating inspection results for a specimen with an adaptive nuisance filter are provided. One method includes selecting a portion of events detected during inspection of a specimen having values for at least one feature of the events that are closer to at least one value of at least one parameter of the nuisance filter than the values for at least one feature of another portion of the events. The method also includes acquiring output of an output acquisition subsystem for the sample of events, classifying the events in the sample based on the acquired output, and determining if one or more parameters of the nuisance filter should be modified based on results of the classifying. The nuisance filter or the modified nuisance filter can then be applied to results of the inspection of the specimen to generate final inspection results for the specimen.

Abstract: Methods and systems for classifying defects detected on a wafer are provided. One method includes inputting information for defects detected on a wafer into each of at least two defect classifiers included in a composite defect classifier. Such a method also includes, for at least one of the defects that is assigned to two or more bins in the composite defect classifier, determining a bin for the at least one of the defects based on a rank assigned to the two or more bins. The rank is assigned to the two or more bins based on one or more characteristics determined for the two or more bins, and the one or more characteristics are determined based on a comparison of predetermined defect classifications for defects in a training set and defect classifications determined for the defects in the training set by the at least two defect classifiers.

Abstract: Systems and methods for discovering defects on a wafer are provided. One method includes detecting defects on a wafer by applying a threshold to output generated by a detector in a first scan of the wafer and determining values for features of the detected defects. The method also includes automatically ranking the features, identifying feature cut-lines to group the defect into bins, and, for each of the bins, determining one or more parameters that if applied to the values for the features of the defects in each of the bins will result in a predetermined number of the defects in each of the bins. The method also includes applying the one or more determined parameters to the output generated by the detector in a second scan of the wafer to generate a defect population that has a predetermined defect count and is diversified in the values for the features.

Abstract: Methods and systems for generating a defect sample for a wafer are provided. One method includes separating defects detected on a wafer into bins having diversity in values of a first set of one or more first attributes of the defects. The method also includes selecting, independently from one or more of the bins, defects within the bins based on diversity in a second set of one or more second attributes of the defects. The selected defects are then used to create a defect sample for the wafer. In this manner, defects having diverse values of multiple attributes can be easily selected.

Abstract: Methods and systems for generating defect samples are provided. One method includes identifying a set of defects detected on a wafer having the most diversity in values of at least one defect attribute and generating different tiles for different defects in the set. The tiles define a portion of all values for the at least one attribute of all defects detected on the wafer that are closer to the values for the at least one attribute of their corresponding defects than the values for the at least one attribute of other defects. In addition, the method includes separating the defects on the wafer into sample bins corresponding to the different tiles based on their values of the at least one attribute, randomly selecting defect(s) from each of two or more of the sample bins, and creating a defect sample for the wafer that includes the randomly selected defects.

Abstract: Methods and systems for setting up a classifier for defects detected on a wafer are provided. One method includes generating a template for a defect classifier for defects detected on a wafer and applying the template to a training data set. The training data set includes information for defects detected on the wafer or another wafer. The method also includes determining one or more parameters for the defect classifier based on results of the applying step.

Abstract: Methods and systems for classifying defects detected on a wafer are provided. One method includes inputting information for defects detected on a wafer into each of at least two defect classifiers included in a composite defect classifier. Such a method also includes, for at least one of the defects that is assigned to two or more bins in the composite defect classifier, determining a bin for the at least one of the defects based on a rank assigned to the two or more bins. The rank is assigned to the two or more bins based on one or more characteristics determined for the two or more bins, and the one or more characteristics are determined based on a comparison of predetermined defect classifications for defects in a training set and defect classifications determined for the defects in the training set by the at least two defect classifiers.

Abstract: Methods and systems for generating unbiased wafer defect samples are provided. One method includes selecting the defects detected by each of multiple scans performed on a wafer that have the most diversity in one or more defect attributes such that a diverse set of defects are selected across each scan. In addition, the method may include selecting the defects such that any defect that is selected and is common to two or more of the scans is not selected twice and any defects that are selected are diverse with respect to the common, selected defect. Furthermore, no sampling, binning, or classifying of the defects may be performed prior to selection of the defects such that the sampled defects are unbiased by any sampling, binning, or classifying method.

Abstract: A system and method of matching multiple scanners using design and defect data are described. A golden wafer is processed using a golden tool. A second wafer is processed using a second tool. Both tools provide focus/exposure modulation. Wafer-level spatial signatures of critical structures for both wafers can be compared to evaluate the behavior of the scanners. Critical structures can be identified by binning defects on the golden wafer having similar patterns. In one embodiment, the signatures must match within a certain percentage or the second tool is characterized as a “no match”. Reticles can be compared in a similar manner, wherein the golden and second wafers are processed using a golden reticle and a second reticle, respectively.

Abstract: Systems and methods for monitoring time-varying classification performance are disclosed.