Abstract: Methods and systems for detecting defects in images of a specimen are provided. One system includes a computer subsystem configured for training an ensemble of deep learning models by altering one or more parameters of the ensemble until a pseudo-loss function determined based on output of the ensemble is approximately equal to but not greater than 0.5. The computer subsystem is also configured for detecting defects in runtime specimen images by inputting the runtime specimen images into the trained ensemble and generating runtime labels for the runtime specimen images indicating if a defect has been detected in the runtime specimen images based on outputs of the deep learning models in the trained ensemble.

Abstract: Systems and methods for detecting defects on a reticle are provided. One system is configured for generating different stacked difference images for multiple instances of first patterned areas in different rows on a wafer based on images generated for the first patterned areas in the different rows. The system is also configured for performing double detection based on the different stacked difference images. The system then identifies defects on the reticle based on the defects detected by the double detection. As described further herein, the systems and methods detect defects from multiple reticle rows printed on a wafer, which can reduce noise and enable detection of substantially small repeater defects. The embodiments are particularly useful for high sensitivity repeater defect detection for extreme ultraviolet (EUV) reticles and multi-die reticles (MDR).

Abstract: Methods and systems for detecting defects on a specimen are provided. One system computes different candidate reference images from different combinations of images of the specimen generated by an inspection subsystem and combines different portions of the candidate reference images without modification to thereby generate a final reference image. The final reference image is then used for defect detection, which may be single or double detection. The embodiments are particularly useful for defect detection in areas of specimens including only non-resolvable, repeating device patterns, like cell regions, but may be used for inspection of other types of areas as well.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes an output acquisition subsystem configured to generate output for a specimen at one or more target locations on the specimen and one or more temperature sensors configured to measure one or more temperatures within the system. The system also includes a deep learning model configured for predicting error in at least one of the one or more target locations based on at least one of the one or more measured temperatures input to the deep learning model by the computer subsystem. The computer subsystem is configured for determining a corrected target location for the at least one of the one or more target locations by applying the predicted error to the at least one of the one or more target locations.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem configured for determining a global texture characteristic of an image of a specimen and one or more local characteristics of a localized area in the image. The system also includes one or more components executed by the computer subsystem. The component(s) include a machine learning model configured for determining information for the specimen based on the global texture characteristic and the one or more local characteristics. The computer subsystem is also configured for generating results including the determined information. The methods and systems may be used for metrology (in which the determined information includes one or more characteristics of a structure formed on the specimen) or inspection (in which the determined information includes a classification of a defect detected on the specimen).

Abstract: Methods and systems for determining information for a specimen are provided. The embodiments described herein are configured for training a machine learning (ML) model for generating higher resolution images of a specimen from images of the specimen generated by an inspection subsystem. The training includes a pre-training step that is performed using only simulated images and a re-training step that is performed using actual images of a test specimen. The higher resolution images generated by the trained ML model from lower resolution inspection images can be used for applications to including nuisance filtering and defect classification.

Abstract: Methods and systems for deep learning alignment for semiconductor applications are provided. One method includes transforming first actual information for an alignment target on a specimen from either design data to a specimen image or a specimen image to design data by inputting the first actual information into a deep generative model such as a GAN. The method also includes aligning the transformed first actual information to second actual information for the alignment target, which has the same information type as the transformed first actual information. The method further includes determining an offset between the transformed first actual information and the second actual information based on results of the aligning and storing the determined offset as an align-to-design offset for use in a process performed on the specimen.

Abstract: Methods and systems for generating a simulated image for a specimen are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a generative adversarial network (GAN), e.g., a conditional GAN (cGAN), trained with a training set that includes portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs. The one or more computer subsystems are configured for generating a simulated image for a specimen by inputting a portion of design data for the specimen into the GAN.

Abstract: Methods and systems for setting up care areas (CAs) for inspection of a specimen are provided. One system includes an imaging subsystem configured for generating images of a specimen and a computer subsystem configured for determining a number of defects detected in predefined cells within one or more of the images generated in a repeating patterned area formed on the specimen. The computer subsystem is also configured for comparing the number of the defects detected in each of two or more of the predefined cells to a predetermined threshold and designating any one or more of the two or more of the predefined cells in which the number of the defects is greater than the predetermined threshold as one or more CAs. In addition, the computer subsystem is configured for storing information for the one or more CAs for use in inspection of the specimen.

Abstract: Systems and methods for setting up a physics-based model are provided. One system includes one or more components that are executed by one or more computer subsystems and that include a physics-based model describing a semiconductor fabrication-related process and a set up component configured for setting up the physics-based model in multiple phases in each of which only a subset of all of the parameters of the physics-based model are set up. A configuration of the set up component is changed between at least two of the multiple phases based on the subset of all of the parameters of the physics-based model set up in the at least two of the multiple phases. The set up component may perform a Bayesian optimization technique for cascaded model set up or calibration using multiple information sources and objective functions.

Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes one or more computer systems and one or more components executed by the one or more computer systems. The component(s) include a deep learning model configured for, for a location on a specimen, generating a gray scale simulated design data image from a high resolution image generated at the location by a high resolution imaging system. The computer system(s) are configured for generating a simulated binary design data image for the location from the gray scale simulated design data image. The computer system(s) are also configured for detecting defects at the location on the specimen by subtracting design data for the location from the simulated binary design data image.

Abstract: Methods and systems for setting up alignment of a specimen are provided. One system includes computer subsystem(s) configured for acquiring two-dimensional (2D) images generated from output of a detector of an output acquisition subsystem at template locations in corresponding areas of printed instances on a specimen. The computer subsystem(s) determine offsets in x and y directions between the template locations using the 2D images and determine an angle of the specimen with respect to the output acquisition subsystem based on the offsets. If the angle is greater than a predetermined value, the computer subsystem(s) rotate the specimen and repeat the steps described above. If the angle is less than the predetermined value, the computer subsystem(s) store one of the 2D images for alignment of the specimen in a process performed on a specimen. The 2D images may include multi-mode images, which may be fused prior to determining the offsets.

Abstract: Methods and systems for setting up inspection of a specimen are provided. One system includes one or more computer subsystems configured for acquiring a reference image for a specimen and modifying the reference image to fit the reference image to a design grid thereby generating a golden grid image. The one or more computer subsystems are also configured for storing the golden grid image for use in inspection of the specimen. The inspection includes aligning a test image of the specimen generated from output of an inspection subsystem to the golden grid image.

Abstract: Methods and systems for detecting charged particles from a specimen are provided. One system includes a first repelling mesh configured to repel charged particles from a specimen having an energy lower than a first predetermined energy and a second repelling mesh configured to repel the charged particles that pass through the first repelling mesh and have an energy that is lower than a second predetermined energy. The system also includes a first attracting mesh configured to attract the charged particles that pass through the first repelling mesh, are repelled by the second repelling mesh, and have an energy that is higher than the first predetermined energy and lower than the second predetermined energy. The system further includes a first detector configured to generate output responsive to the charged particles that pass through the first attracting mesh.

Abstract: Methods and systems for detecting defects on a mask are provided. One method includes generating a database reference image for a multi-die mask by simulation and detecting first defects on the mask by comparing the database reference image to images of the mask generated by an imaging subsystem for a first of the multiple dies. The method also includes generating a die reference image for the first of the multiple dies by applying one or more parameters of the imaging subsystem learned by generating the database reference image to the images generated by the imaging subsystem of one or more of the multiple dies other than the first multiple die. In addition, the method includes detecting second defects on the mask by comparing the die reference image to the images of the mask generated by the imaging subsystem for the first of the multiple dies.

Abstract: Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes an inspection subsystem configured to generate images of a specimen and one or more computer subsystems configured for detecting defect candidates on the specimen. Detecting the defect candidates includes identifying a patterned feature in a test image included in the images generated of the specimen. Detecting the defect candidates also includes, for at least one pixel in the test image located within the patterned feature, determining a difference between a characteristic of the at least one pixel and the characteristic of other pixels in the test image located within a predetermined window of the at least one pixel. In addition, detecting the defect candidates includes detecting a defect candidate at the at least one pixel based on the determined difference.

Abstract: Methods and systems for selecting defect detection methods for inspection of a specimen are provided. One system includes one or more computer subsystems configured for separating polygons in a care area into initial sub-groups based on a characteristic of the polygons on the specimen and determining a characteristic of noise in output generated by a detector of an inspection subsystem for the polygons in the different initial sub-groups. The computer subsystem(s) are also configured for determining final sub-groups for the polygons by combining any two or more of the different initial sub-groups having substantially the same values of the characteristic of the noise. In addition, the computer subsystem(s) are configured for selecting first and second defect detection methods for application to the output generated by the detector of the inspection subsystem during inspection of the specimen or another specimen.

Abstract: Methods and systems for controlling pressure in a cavity of a light source are provided. One system includes a barometric pressure sensor configured for measuring pressure in a cavity of a light source. The system also includes one or more gas flow elements configured for controlling an amount of one or more gases in the cavity. In addition, the system includes a control subsystem configured for comparing the measured pressure to a predetermined range of values for the pressure and, when the measured pressure is outside of the predetermined range, altering a parameter of at least one of the one or more gas flow elements based on results of the comparing.

Abstract: Methods and systems for aligning images of a specimen generated with different modes of an imaging subsystem are provided. One method includes separately aligning first and second images generated with first and second modes, respectively, to a design for the specimen. For a location of interest in the first image, the method includes generating a first difference image for the location of interest and the first mode and generating a second difference image for the location of interest and the second mode. The method also includes aligning the first and second difference images to each other and determining information for the location of interest from results of the aligning.