Abstract: A method for processing images for metrology using a charged particle beam tool may include obtaining, from the charged particle beam tool, an image of a portion of a sample. The method may further include processing the image using a first image processing module to generate a processed image. The method may further include determining image quality characteristics of the processed image and determining whether the image quality characteristics of the processed image satisfy predetermined imaging criteria. The method may further include in response to the image quality characteristics of the processed image not satisfying the imaging criteria, updating a tuning condition of the charged-particle beam tool, acquiring an image of the portion of the sample using the charged-particle beam tool that has the updated tuning condition, and processing the acquired image using the first image processing module to enable the processed acquired image to satisfy the predetermined imaging criteria.

Abstract: Systems and methods for training a machine learning model for defect detection include obtaining training data including an inspection image of a fabricated integrated circuit (IC) and design layout data of the IC, and training a machine learning model using the training data. The machine learning model includes a first autoencoder and a second autoencoder. The first autoencoder includes a first encoder and a first decoder. The second autoencoder includes a second encoder and a second decoder. The second decoder is configured to obtain a first code outputted by the first encoder. The first decoder is configured to obtain a second code outputted by the second encoder.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: A system and method for defect inspection using voltage contrast in a charged particle system are provided. Some embodiments of the system and method include positioning the stage at a first position to enable a first beam of the plurality of beams to scan a first surface area of the wafer at a first time to generate a first image associated with the first surface area; positioning the stage at a second position to enable a second beam of the plurality of beams to scan the first surface area at a second time to generate a second image associated with the first surface area; and comparing the first image with the second image to enable detecting whether a defect is identified in the first surface area of the wafer.

Abstract: Apparatuses, systems, and methods for providing beams for defect detection and defect location identification associated with a sample of charged particle beam systems. In some embodiments, a method may include obtaining an image of a sample; determining defect characteristics from the image; generating an updated image based on the determined defect characteristics and the image; and aligning the updated image with a reference image.

Abstract: An improved apparatus and method for enhancing an image, and more particularly an apparatus and method for enhancing an image through cross-talk cancellation in a multiple charged-particle beam inspection are disclosed. An improved method for enhancing an image includes acquiring a first image signal of a plurality of image signals from a detector of a multi-beam inspection system. The first image signal corresponds to a detected signal from a first region of the detector on which electrons of a first secondary electron beam and of a second secondary electron beam are incident. The method includes reducing, from the first image signal, cross-talk contamination originating from the second secondary electron beam using a relationship between the first image signal and beam intensities associated with the first secondary electron beam and the second secondary electron beam. The method further includes generating a first image corresponding to first secondary electron beam after reduction.

Abstract: Systems and methods for image analysis include obtaining a plurality of simulation images and a plurality of non-simulation images both associated with a sample under inspection, at least one of the plurality of simulation images being a simulation image of a location on the sample not imaged by any of the plurality of non-simulation images; and training an unsupervised domain adaptation technique using the plurality of simulation images and the plurality of non-simulation images as inputs to reduce a difference between first intensity gradients of the plurality of simulation images and second intensity gradients of the plurality of non-simulation images.

Abstract: An improved systems and methods for correcting distortion of an inspection image are disclosed. An improved method for correcting distortion of an inspection image comprises acquiring an inspection image, aligning a plurality of patches of the inspection image based on a reference image corresponding to the inspection image, evaluating, by a machine learning model, alignments between each patch of the plurality of patches and a corresponding patch of the reference image, determining local alignment results for the plurality of patches of the inspection image based on a reference image corresponding to the inspection image, determining an alignment model based on the local alignment results, and correcting a distortion of the inspection image based on the alignment model.

Abstract: Systems and methods for detecting a defect on a sample include receiving a first image and a second image associated with the first image; determining, using a clustering technique, N first feature descriptor(s) for L first pixel(s) in the first image and M second feature descriptor(s) for L second pixel(s) in the second image, wherein each of the L first pixel(s) is co-located with one of the L second pixel(s), and L, M, and N are positive integers; determining K mapping probability between a first feature descriptor of the N first feature descriptor(s) and each of K second feature descriptor(s) of the M second feature descriptor(s), wherein K is a positive integer; and providing an output for determining whether there is existence of an abnormal pixel representing a candidate defect on the sample based on a determination that one of the K mapping probability does not exceed a threshold value.

Abstract: An improved apparatus and method for enhancing an image, and more particularly an apparatus and method for enhancing an image through cross-talk cancellation in a multiple charged-particle beam inspection are disclosed. An improved method for enhancing an image includes acquiring a first image signal of a plurality of image signals from a detector of a multi-beam inspection system. The first image signal corresponds to a detected signal from a first region of the detector on which electrons of a first secondary electron beam and of a second secondary electron beam are incident. The method includes reducing, from the first image signal, cross-talk contamination originating from the second secondary electron beam using a relationship between the first image signal and beam intensities associated with the first secondary electron beam and the second secondary electron beam. The method further includes generating a first image corresponding to first secondary electron beam after reduction.

Abstract: Systems and methods for in-die metrology using target design patterns are provided. These systems and methods include selecting a target design pattern based on design data representing the design of an integrated circuit, providing design data indicative of the target design pattern to enable design data derived from the target design pattern to be added to second design data, wherein the second design data is based on the first design data. Systems and methods can further include causing structures derived from the second design data to be printed on a wafer, inspecting the structures on the wafer using a charged-particle beam tool, and identifying metrology data or process defects based on the inspection. In some embodiments the systems and methods further include causing the charged-particle beam tool, the second design data, a scanner, or photolithography equipment to be adjusted based on the identified metrology data or process defects.

Abstract: An improved systems and methods for generating a synthetic defect image are disclosed. An improved method for generating a synthetic defect image comprises acquiring a machine learning-based generator model; providing a defect-free inspection image and a defect attribute combination as inputs to the generator model; and generating by the generator model, based on the defect-free inspection image, a predicted synthetic defect image with a predicted defect that accords with the defect attribute combination.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: A method for aligning a wafer image with a reference image, comprising: searching for a targeted reference position on the wafer image for aligning the wafer image with the reference image; and in response to a determination that the targeted reference position does not exist: defining a current lock position and an area that encloses the current lock position on the wafer image; computing an alignment score of the current lock position; comparing the alignment score of the current lock position with stored alignment scores of positions previously selected in relation to aligning the wafer image with the reference image; and aligning the wafer image with the reference image based on the comparison.

Abstract: Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Abstract: Systems and methods for optimal electron beam metrology guidance are disclosed. According to certain embodiments, the method may include receiving an acquired image of a sample, determining a set of image parameters based on an analysis of the acquired image, determining a set of model parameters based on the set of image parameters, generating a set of simulated images based on the set of model parameters. The method may further comprise performing measurement of critical dimensions on the set of simulated images and comparing critical dimension measurements with the set of model parameters to provide a set of guidance parameters based on comparison of information from the set of simulated images and the set of model parameters. The method may further comprise receiving auxiliary information associated with target parameters including critical dimension uniformity.

Abstract: An improved method and apparatus for enhancing an inspection image in a charged-particle beam inspection system. An improved method for enhancing an inspection image comprises acquiring a first image and a second image of multiple stacked layers of a sample that are taken with a first focal point and a second focal point, respectively, associating a first segment of the first image with a first layer among the multiple stacked layers and associating a second segment of the second image with a second layer among the multiple stacked layers, updating the first segment based on a first reference image corresponding to the first layer and updating the second segment based on a second reference image corresponding to the second layer, and combining the updated first segment and the updated second segment to generate a combined image including the first layer and the second layer.

Abstract: An improved apparatus and method for extracting pattern contour information from an inspection image in a multiple charged-particle beam inspection system are disclosed. An improved method for extracting pattern contour information from an inspection image comprises identifying, from an inspection image obtained from a charged-particle beam inspection system, a first pattern and a second pattern that partially overlap in the inspection image. The method also comprises generating a first separation image by removing an image area corresponding to the second pattern from the inspection image. The first separation image includes the first pattern of which a first part is removed when removing the image area corresponding to the second pattern. The method also comprises updating the first separation image to include image data representing the removed first part of the first pattern based on a first reference image corresponding to the first pattern.

Abstract: Systems and methods for in-die metrology using target design patterns are provided. These systems and methods include selecting a target design pattern based on design data representing the design of an integrated circuit, providing design data indicative of the target design pattern to enable design data derived from the target design pattern to he added to second design data, wherein the second design data is based on the first design data. Systems and methods can further include causing structures derived from the second design data to be printed on a wafer, inspecting the structures on the wafer using a charged-particle beam tool, and identifying metrology data or process defects based on the inspection. In some embodiments the systems and methods further include causing the charged-particle beam tool, the second design data, a scanner, or photolithography equipment to be adjusted based on the identified metrology data or process defects.