Abstract:
Methods and systems for determining characteristic(s) of patterns of interest (POIs) are provided. One system is configured to acquire output of an inspection system generated at the POI instances without detecting defects at the POI instances. The output is then used to generate a selection of the POI instances. The system then acquires output from an output acquisition subsystem for the selected POI instances. The system also determines characteristic(s) of the POI using the output acquired from the output acquisition subsystem.
Abstract:
Methods and systems for determining coordinates for an area of interest on a specimen are provided. One system includes one or more computer subsystems configured for, for an area of interest on a specimen being inspected, identifying one or more targets located closest to the area of interest. The computer subsystem(s) are also configured for aligning one or more images for the one or more targets to a reference for the specimen. The image(s) for the target(s) and an image for the area of interest are acquired by an inspection subsystem during inspection of the specimen. The computer subsystem(s) are further configured for determining an offset between the image(s) for the target(s) and the reference based on results of the aligning and determining modified coordinates of the area of interest based on the offset and coordinates of the area of interest reported by the inspection subsystem.
Abstract:
Hybrid inspectors are provided. One system includes computer subsystem(s) configured for receiving optical based output and electron beam based output generated for a specimen. The computer subsystem(s) include one or more virtual systems configured for performing one or more functions using at least some of the optical based output and the electron beam based output generated for the specimen. The system also includes one or more components executed by the computer subsystem(s), which include one or more models configured for performing one or more simulations for the specimen. The computer subsystem(s) are configured for detecting defects on the specimen based on at least two of the optical based output, the electron beam based output, results of the one or more functions, and results of the one or more simulations.
Abstract:
A voltage contrast imaging defect detection system includes a voltage contrast imaging tool and a controller coupled to the voltage contrast imaging tool. The controller is configured to generate one or more voltage contrast imaging metrics for one or more structures on a sample, determine one or more target areas on the sample based on the one or more voltage contrast imaging metrics, receive a voltage contrast imaging dataset for the one or more target areas on the sample from the voltage contrast imaging tool, and detect one or more defects based on the voltage contrast imaging dataset.
Abstract:
Generalized virtual inspectors are provided. One system includes two or more actual systems configured to perform one or more processes on specimen(s) while the specimen(s) are disposed within the actual systems. The system also includes one or more virtual systems coupled to the actual systems to thereby receive output generated by the actual systems and to send information to the actual systems. The virtual system(s) are configured to perform one or more functions using at least some of the output received from the actual systems. The virtual system(s) are not capable of having the specimen(s) disposed therein.
Abstract:
Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.
Abstract:
Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.
Abstract:
Methods and systems for performing active learning for defect classifiers are provided. One system includes one or more computer subsystems configured for performing active learning for training a defect classifier. The active learning includes applying an acquisition function to data points for the specimen. The acquisition function selects one or more of the data points based on uncertainty estimations associated with the data points. The active learning also includes acquiring labels for the selected one or more data points and generating a set of labeled data that includes the selected one or more data points and the acquired labels. The computer subsystem(s) are also configured for training the defect classifier using the set of labeled data. The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem.
Abstract:
Methods and systems for detecting defects on a specimen are provided. One system includes a storage medium configured for storing images for a physical version of a specimen generated by an inspection system. At least two dies are formed on the specimen with different values of one or more parameters of a fabrication process performed on the specimen. The system also includes computer subsystem(s) configured for comparing portions of the stored images generated at locations on the specimen at which patterns having the same as-designed characteristics are formed with at least two of the different values. The portions of the stored images that are compared are not constrained by locations of the dies on the specimen, locations of the patterns within the dies, or locations of the patterns on the specimen. The computer subsystem(s) are also configured for detecting defects at the locations based on results of the comparing.
Abstract:
A metrology system includes a controller communicatively coupled to a metrology tool. The controller may generate a three-dimensional model of a sample, generate a predicted metrology image corresponding to a predicted analysis of the sample with the metrology tool based on the three-dimensional model, evaluate two or more candidate metrology recipes for extracting the metrology measurement from the one or more predicted metrology images, select, based on one or more selection metrics, a metrology recipe from the two or more candidate metrology recipes for extracting a metrology measurement from an image of the structure from the metrology tool, receive an output metrology image of a fabricated structure from the metrology tool based on a metrology measurement of the fabricated structure, and extract the metrology measurement associated with the fabricated structure from the output metrology image based on the metrology recipe.