Abstract: Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a setup deep learning (DL) model configured for separately performing defect detection for a specimen based on output generated for the specimen by each of two or more modes of an inspection system, respectively, and separately re-performing defect detection for the specimen based on masked output generated for each of the modes, respectively. The computer subsystem determines a difference between results of separately performing and separately re-performing the defect detections for each of the modes, respectively, and identifies a subset of the modes for which the difference is larger than other modes as candidate mode(s) for inspection of the specimen.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a pre-trained defect generative pre-trained transformer (DefectGPT) encoder configured for determining information for a specimen based on one or more inputs specific to the specimen. The computer subsystem is configured for inputting the one or more inputs into the pre-trained DefectGPT encoder.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a setup deep learning (DL) model configured for separately performing defect detection for a specimen based on output generated for the specimen by each of two or more modes of an inspection system, respectively, and separately re-performing defect detection for the specimen based on masked output generated for each of the modes, respectively. The computer subsystem determines a difference between results of separately performing and separately re-performing the defect detections for each of the modes, respectively, and identifies a subset of the modes for which the difference is larger than other modes as candidate mode(s) for inspection of the specimen.

Abstract: Methods and systems for performing functions with protected data sources from different entities are provided. One system includes a virtual system coupled to an actual system to thereby receive output generated by the actual system for a physical version of the specimen while the specimen is disposed within the actual system. The virtual system includes at least a computer system and a storage medium. The virtual system is not capable of having the physical version of the specimen disposed therein. The virtual system is configured for performing one or more functions for the specimen with two or more protected data sources from two or more different entities, respectively. The virtual system is also configured for performing a virtual version of a process capable of being performed by the actual system for the physical version of the specimen.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a deep learning (DL) model trained without labeled data (e.g., in an unsupervised or self-supervised manner) and configured to generate a reference for a specimen from one or more inputs that include at least a specimen image or data generated from the specimen image. The computer subsystem is configured for determining information for the specimen from the reference and at least the specimen image or the data generated from the specimen image.

Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.

Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include a deep learning (DL) model trained without labeled data (e.g., in an unsupervised or self-supervised manner) and configured to generate a reference for a specimen from one or more inputs that include at least a specimen image or data generated from the specimen image. The computer subsystem is configured for determining information for the specimen from the reference and at least the specimen image or the data generated from the specimen image.

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 determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include multiple deep learning (DL) models configured for determining information for a specimen based on output generated by the specimen with learning mode(s) of an imaging subsystem. The one or more components also include a knowledge distillation component configured for combining output generated by the multiple DL models. In addition, the one or more components include a final knowledge distilled DL model configured for determining information for the specimen or an additional specimen based on output generated for the specimen or the additional specimen with runtime mode(s) of the imaging subsystem. Before the final KD DL model determines the information, the knowledge distillation component is configured for supervised training of the final knowledge distilled DL model using the combined output.

Abstract: Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

Abstract: Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

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 learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.

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: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.

Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.

Abstract: Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

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.