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: 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: 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 performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.

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 performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.

Abstract: An inspection system for detecting anomalies on a substrate. The inspection system has a sensor array for generating image data. A first high speed network is coupled to the sensor array and receives and communicates the image data. An array of process nodes is coupled to the first high speed network, and receives and processes the image data to produce anomaly reports. Each process node has an interface card coupled to the first high speed network, that receives the image data from the first high speed network and formats the image data according to a high speed interface bus protocol. The interface card sets a register indicating whether a predetermined amount of image data has been stored in a memory, and the process node reads the register to determine whether the predetermined amount of image data has been stored in the memory, and initiates image processing when the register indicates that the predetermined amount of image data has been stored in the memory.

Abstract: An inspection system for detecting anomalies on a substrate. The inspection system has a sensor array for generating image data. A first high speed network is coupled to the sensor array and receives and communicates the image data. An array of process nodes is coupled to the first high speed network, and receives and processes the image data to produce anomaly reports. Each process node has an interface card coupled to the first high speed network, that receives the image data from the first high speed network and formats the image data according to a high speed interface bus protocol. A high speed interface bus is coupled to the interface card, receives the image data from the interface card. A computer is coupled to the high speed interface bus, and receives the image data from the high speed interface bus and processes the image data according to an algorithm, to produce the anomaly report.

Abstract: An inspection system for detecting anomalies on a substrate. The inspection system has a sensor array for generating image data. A first high speed network is coupled to the sensor array and receives and communicates the image data. An array of process nodes is coupled to the first high speed network, and receives and processes the image data to produce anomaly reports. Each of the process nodes has an amount of memory that is sufficient to receive image data representing a plurality of dice on an integrated circuit wafer, and each of the process nodes performs analysis on the plurality of dice. Each process node has an interface card coupled to the first high speed network, that receives the image data from the first high speed network and formats the image data according to a high speed interface bus protocol. A high speed interface bus is coupled to the interface card, receives the image data from the interface card.