Abstract: Methods and systems for decision tree construction for automatic classification of defects on semiconductor wafers are provided. One method includes creating a decision tree for classification of defects detected on a wafer by altering one or more floating trees in the decision tree. The one or more floating trees are sub-trees that are manipulated as individual units. In addition, the method includes classifying the defects detected on the wafer by applying the decision tree to the defects.

Abstract: Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.

Abstract: Methods and systems for decision tree construction for automatic classification of defects on semiconductor wafers are provided. One method includes creating a decision tree for classification of defects detected on a wafer by altering one or more floating trees in the decision tree. The one or more floating trees are sub-trees that are manipulated as individual units. In addition, the method includes classifying the defects detected on the wafer by applying the decision tree to the defects.

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

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

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: 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: 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: Computer-implemented methods for performing one or more defect-related functions are provided. One method for identifying noise in inspection data includes identifying events detected in a number of sets of inspection data that is less than a predetermined number as noise. One method for binning defects includes binning the defects into groups based on defect characteristics and the sets of the inspection data in which the defects were detected. One method for selecting defects for defect analysis includes binning defects into group(s) based on proximity of the defects to each other and spatial signatures formed by the group(s). A different method for selecting defects for defect analysis includes selecting defects having the greatest diversity of defect characteristic(s) for defect analysis. One method includes classifying defects on a specimen using inspection data generated for the specimen combined with defect review data generated for the specimen.

Abstract: Hybrid methods for classifying defects in semiconductor manufacturing are provided. The methods include applying a flexible sequence of rules for defects to inspection data. The sequence of rules includes deterministic rules, statistical rules, hybrid rules, or some combination thereof. The rules included in the sequence may be selected by a user using a graphical interface. The method also includes classifying the defects based on results of applying the sequence of rules to the inspection data.

Abstract: Hybrid methods for classifying defects in semiconductor manufacturing are provided. The methods include applying a flexible sequence of rules for defects to inspection data. The sequence of rules includes deterministic rules, statistical rules, hybrid rules, or some combination thereof. The rules included in the sequence may be selected by a user using a graphical interface The method also includes classifying the defects based on results of applying the sequence of rules to the inspection data.

Abstract: A jet engine suspension includes a master component for attachment, fixed to an aircraft pylon. In the event of breakage of the master component and in the situation where the fixing screws are no longer able to transmit force, a peg is provided which resists transmitted loads such that the master component collapses until an upper shoulder rests on the pylon and supports the master component. The peg is free of dynamic stress in normal service and there is absolutely no risk of it cracking in fatigue.

Abstract: A jet engine suspension comprises a master component for attachment, fixed to an aircraft pylon. If it breaks and if the situation arises where the fixing screws are no longer able to transmit force, a peg takes over: the master component collapses until an upper shoulder rests on the pylon and supports this component. The peg is free of dynamic stress in normal service and there is absolutely no risk of it cracking in fatigue.