Abstract: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: A computerized method for categorizing defects on a substrate. A list of defects on the substrate is received as input to a processor, where each defect is represented by a defect location and an associated micro-defect code. The input is analyzed with the processor to detect spatial clusters of defects on the substrate. The spatial clusters are analyzed with the processor to determine which of the spatial clusters represent known macro-defects and which of the spatial clusters represent unknown macro-defects. The micro-defect code associated with each defect that is included in one of the spatial clusters that is determined to be a known macro-defect is changed with the processor with a macro-defect code that is associated solely with the known macro-defect. The processor analyzes the defects that are included in one of the spatial clusters that is determined to be an unknown macro-defect to determine a predominantly occurring micro-defect code.

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

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: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: Methods and systems for generating an inspection process for an inspection system are provided. One computer implemented method includes generating inspection data for a selected defect on a specimen at different values of one or more image acquisition parameters of the inspection system. The method also includes determining which of the different values produces the best inspection data for the selected defect. In addition, the method includes selecting the different values determined to produce the best inspection data as values of the one or more image acquisition parameters to be used for the inspection process.

Abstract: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: Wafer inspection systems and methods are provided. One inspection system includes a module measurement cell coupled to a host inspection system by a wafer handler. The module measurement cell is configured to inspect a wafer using one or more modes prior to inspection of the wafer by the host inspection system. The one or more modes include backside inspection, edge inspection, frontside macro defect inspection, or a combination thereof. Another inspection system includes two or more low resolution electronic sensors arranged at multiple viewing angles. The sensors are configured to detect light returned from a wafer substantially simultaneously. A method for analyzing inspection data includes selecting a template corresponding to a support device that contacts a backside of a wafer prior to inspection of the backside of the wafer. The method also includes subtracting data representing the template from inspection data generated by inspection of the backside of the wafer.

Abstract: A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature.

Abstract: Wafer inspection systems and methods are provided. One inspection system includes a module measurement cell coupled to a host inspection system by a wafer handler. The module measurement cell is configured to inspect a wafer using one or more modes prior to inspection of the wafer by the host inspection system. The one or more modes include backside inspection, edge inspection, frontside macro defect inspection, or a combination thereof. Another inspection system includes two or more low resolution electronic sensors arranged at multiple viewing angles. The sensors are configured to detect light returned from a wafer substantially simultaneously. A method for analyzing inspection data includes selecting a template corresponding to a support device that contacts a backside of a wafer prior to inspection of the backside of the wafer. The method also includes subtracting data representing the template from inspection data generated by inspection of the backside of the wafer.

Abstract: A method for analyzing defects on a substrate, including inspecting the substrate to detect the defects, identifying the defects by location, analyzing the defects to detect extended objects, and analyzing the extended objects for repetition across the substrate. Thus, the present invention extends beyond the present analysis methods, by analyzing the extended objects for repetition across the substrate. In this manner, correlation with processing problems can be more readily detected, in cases where the individual defects themselves, of which the extended objects are formed, do not appear to have repeating properties.

Abstract: A system for determining an assigned classification for a set of physical events on a substrate. Sensors sense the physical events on the substrate and produce event data. A plug in rule module manager receives and manages any number of plug in rule modules. Each plug in rule module has an input, a local filter, an analyzer, and an output. The input receives the event data and confidence values from preceding plug in rule modules. The local filter analyzes the received confidence values from the preceding plug in rule modules and selectively by passes the plug in rule module based at least in part upon the received confidence values from the preceding plug in rule modules. The analyzer analyzes the event data in view of a given classification associated with the plug in rule module, and assigns a confidence value based at least in part upon how well the event data fits the given classification. The output provides the confidence value to subsequent plug in rule modules.