Abstract: To provide a novel and improved information processing device that can make more efficient an inspection performed by a flying body capable of performing imaging. Provided is an information processing device including an imaging position information acquisition unit configured to acquire imaging position information at a time when a structure is imaged which is acquired by an imaging device configured to fly over a periphery of die structure to image the structure on the basis of certain flight information, and a damage data generating unit configured to use a captured image of the structure imaged by the imaging device and the imaging position information and to generate data related to damage of the structure including position information of damage of the structure included in the captured image.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: An image processing apparatus comprises: an obtaining unit configured to obtain an image and distance information concerning a distance from an in-focus plane, which corresponds to each pixel included in the image; a setting unit configured to set an image processing condition according to the distance information based on an output characteristic of an output apparatus concerning a sharpness; and a processing unit configured to perform image processing for the image using the distance information obtained by the obtaining unit and the image processing condition set by the setting unit, wherein the processing unit changes, in accordance with the distance information, a band of a spatial frequency of the image to which the image processing is applied.

Abstract: An information processing method includes obtaining information on a deformation factor of a surface of a target substrate; obtaining a surface image of the target substrate; calculating a correction coefficient for correcting an image change due to deformation of the surface, based on the information on the deformation factor of the surface; and generating a corrected image of the target substrate by correcting the surface image of the target substrate using the correction coefficient.

Abstract: Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

Abstract: The present invention relates to a method for superimposing at least two images of a photolithographic mask, wherein the method comprises the following steps: (a) determining at least one first difference of at least one first image relative to design data of the photolithographic mask; (b) determining at least one second difference of at least one second image relative to design data of the photolithographic mask, or relative to the at least one first image; and (c) superimposing the at least one first image and the at least one second image taking account of the at least one first difference and the at least one second difference.

Abstract: Methods and systems for improved regularization associated with tomographically resolved image based measurements of semiconductor structures are presented herein. The regularizations described herein are based on measurement data and parameterization of a constrained voxel model that captures known process variations. The constrained voxel model is determined based on simplified geometric models, process models, or both, characterizing the structure under measurement. A constrained voxel model has dramatically fewer degrees of freedom compared to an unconstrained voxel model. The value associated with each voxel of the constrained voxel model depends on a relatively small number of independent variables. Selection of the independent variables is informed by knowledge of the structure and the underlying fabrication process. Regularization based on a constrained voxel model enables faster convergence and a more accurate reconstruction of the measured structure with less computational effort.

Abstract: An image processing apparatus acquires a first pixel size and a second pixel size that are pixel sizes in a predetermined axis direction of a first image and a second image captured at different points in time, respectively, determines whether the first pixel size and second pixel size differ from each other, and decides, if the first pixel size differs from the second pixel size, a size in a predetermined axis direction of a comparison area based on a larger one of the first pixel size and the second pixel size. The comparison area includes a plurality of gray levels, and is compared to a gray level of a position of interest in one of the first and second image, and the comparison area existing in the other of the first and second image, different from the one image.

Abstract: An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

Abstract: A method of inspecting images on printed products by a computer in a printing machine. Printed products are recorded and digitized by an image sensor of an image inspection system in the course of the image inspection process, and the computer compares them to a digital reference image. If deviations are found, the defective printed products are removed. The computer analyzes the deviations found in the course of the image inspection process together with further data from other system parts and from the machine, determines specific defect classes and the causes thereof based on the defects by machine learning processes, assigns the defects found in the image inspection process to the defect classes in a corresponding way, and displays the classified detected defects with their defect classes and causes to an operator of the machine so that the operator can initiate specific measures to eliminate the defect causes.

Abstract: A thickness estimating apparatus includes a transfer robot, a light source, a camera, a memory and a controller. The memory stores a thickness predicting model generated based on a data set including a thickness of at least one of a test wafer corresponding to the wafer or a test element layer formed on the test wafer, and the thickness predicting model being trained to minimize a loss function of the data set. The controller applies pixel data, which is acquired from at least one pixel selected from a plurality of pixels included in a captured image, to the thickness predicting model, to predict a thickness of at least one of the wafer or an element layer formed on the wafer in a position corresponding to a position of the selected pixel.

Abstract: A method and system for automated visual inspection, include receiving, from a camera imaging an inspection line, an image of the inspection line. The image includes an item on the inspection line personal or confidential image data. A processor produces from the image of the inspection line a reduced image, which does not include the personal or confidential image data, and inputs the reduced image to an inspection process.

Abstract: A method for determining measurement data of a printed pattern on a substrate. The method involves obtaining (i) images of the substrate including a printed pattern corresponding to a reference pattern, (ii) an averaged image of the images, and (iii) a composite contour based on the averaged image. Further, the composite contour is aligned with respect to a reference contour of the reference pattern and contours are extracted from the images based on both the aligned composite contour and the output of die-to-database alignment of the composite contour. Further, the method determines a plurality of pattern measurements based on the contours and the measurement data corresponding to the printed patterns based on the plurality of the pattern measurements. Further, the method determines a one or more process variations such as stochastic variation, inter-die variation, intra-die variation and/or total variation.

Abstract: An apparatus includes: a processed-image acquisition part for acquiring a processed image including image information of a surface of a substrate having a target film formed thereon; an estimation part for estimating a shape characteristic value of the target film, by applying an estimation model for estimating the shape characteristic value of the target film to the processed image; an underlying influence model creation part for creating an underlying influence model associated with a correlation between a difference between an estimation result of the shape characteristic value of the target film and a shape characteristic value of the target film acquired without using the estimation model, and information related to a color of a surface of an underlying substrate before the target film is formed, and an estimated-result correction part for correcting the estimation result of the shape characteristic value of the target film based on the underlying influence model.

Abstract: There are provided system and method of classifying defects in a specimen. The method includes: obtaining one or more defect clusters detected on a defect map of the specimen, each cluster characterized by a set of cluster attributes comprising spatial attributes including spatial density indicative of density of defects in one or more regions accommodating the cluster, each given defect cluster being detected at least based on the spatial density thereof meeting a criterion. The defect map also comprises non-clustered defects. Defects of interest (DOI) are identified in each cluster by performing respective defect filtrations for each cluster and non-clustered defects.

Abstract: A system and method for generating a quality metric relating to a fully or partially fabricated semiconductor device wafer (FPFSDW), the method including providing a spot map correlating a plurality of reference field images (RFIs) to a corresponding plurality of reference spot locations (RSLs) on at least one reference structure formed on a reference semiconductor device wafer, taking a measurement of at least a portion of at least one FPFSDW structure formed on the FPFSDW, thereby generating a measurement field image (MFI) of at least a portion of the at least one FPFSDW structure and a pupil image of the at least a portion of the at least one FPFSDW structure, identifying, for the measurement, a measurement spot location (MSL) on the at least one FPFSDW structure, using the MFI and the spot map, and generating a quality metric of the FPFSDW, using the pupil image and the MSL.

Abstract: There is a need for more effective and efficient printed circuit board (PCB) design. This need can be addressed by, for example, solutions for performing automated PCB component estimation. In one example, a method includes identifying a plurality of initial component estimations for the PCB; performing a shadow detection segmentation using the plurality of initial component estimations, a non-direct-lighting image, and one or more direct-lighting images to generate a first set of detected PCB components; performing a super-pixel segmentation using the plurality of initial component estimations and the non-direct-lighting-image to generate a second set of detected PCB components; and generating a bill of materials for the PCB based at least in part on the first set of detected PCB components and the second set of detected PCB components.

Abstract: Systems and methods for providing access to a document within a first file structure for one or more users of a content management system. Location for the content object within a file plan of a records management system is provided, in response to detecting a declaration of the document as a record. The content object is moved from the first file structure to the location in the file plan. A record marker is created in the first file structure to identify a navigation path from the first file structure to the location of the content object in the file plan.

Abstract: Methods and systems for diagnosing a semiconductor wafer are provided. A plurality of raw images of the semiconductor wafer are obtained according to GDS information regarding a layout of a target die, by an inspection apparatus. A first image-based comparison is performed on the raw images, so as to provide a comparison result, by a determining circuitry. The comparison result indicates whether an image difference is present between the images. One of the raw images having the image difference is assigned as a defect image. A second image-based comparison is performed on a reference image and the defect image, so as to classify a defect type of the image difference, by the determining circuitry. The layout of the target die includes a circuit with a duplicate layout formed by a plurality of same cells. The number of the plurality of raw images is greater than 2.

Abstract: A method for improving a process model by measuring a feature on a printed design that was constructed based in part on a target design is disclosed. The method includes obtaining a) an image of the printed design from an image capture device and b) contours based on shapes in the image. The method also includes identifying, by a pattern recognition program, patterns on the target design that include the feature and determining coordinates, on the contours, that correspond to the feature. The method further includes improving the process model by at least a) providing a measurement of the feature based on the coordinates and b) calibrating the process model based on a comparison of the measurement with a corresponding feature in the target design.

Abstract: Methods and systems for controlling a process for inspection of a specimen are provided. One system includes one or more computer subsystems configured for determining a statistical characteristic of difference images generated for multiple instances of a care area on a specimen and determining variation in the statistical characteristic compared to a statistical characteristic of difference images generated for multiple instances of the care area on one or more other specimens. In addition, the one or more computer subsystems are configured for determining one or more changes to one or more parameters used for detecting defects in the care area on the specimen based on the variation.

Abstract: In a method for determining the T1 time and also of at least one tissue proportion per voxel in a predetermined volume segment of an examination object with a magnetic resonance (MR) sequence: a radio frequency (RF) preparation pulse is radiated in; a readout module is repeatedly run after the RF preparation pulse to acquire MR data; and the T1 time and the at least one tissue proportion per voxel is determined as a function of the MR data. The readout module can include: an RF excitation pulse at a beginning of the readout module, a phase encoding gradient, and a number of readout gradients (3a-3g) for acquiring the MR data. During running of the readout module, the MR data may be acquired, at least at times, with more than two echoes.

Abstract: A method for determining a cause of a mounting failure for a component mounted on a substrate, which is performed by an electronic apparatus, comprises: receiving an inspection result of a mounting failure for each of a plurality of first components determined by inspecting a plurality of substrates of a first type; calculating a mounting failure rate of each of the plurality of first components using the inspection result; determining a plurality of second components in which a mounting failure has occurred based on the mounting failure rate; and determining a cause of the mounting failure for each of the plurality of second components as at least one of a component mounting position setting error, a mounting condition setting error according to a component type and a defect of a nozzle, based on the mounting failure rate of each of the plurality of first components.

Abstract: Provided is an inspection apparatus including: an irradiation source irradiating a first pattern formed on an inspection target object with an electron beam; a detection circuit acquiring a first inspection image generated from the first pattern by irradiation; a filter circuit performing smoothing using a local region having a first size in a direction parallel to a first outline included in the first inspection image and a second size smaller than the first size in a direction perpendicular to the first outline and acquiring a second inspection image including a second outline generated by the smoothing; and a comparison circuit comparing the second inspection image with a predetermined reference image.

Abstract: A system of classifying a pattern of interest (POI) on a semiconductor specimen, where the system includes a processor and memory circuitry configured to obtain a high-resolution image of the POI, and generate data usable for classifying the POI in accordance with a defectiveness-related classification. Generating the data utilizes a machine learning model that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI, and a label associated with the image. The label is derivative of low-resolution inspection of the respective training pattern.

Abstract: An image generation device and an appearance inspection device which can increase the number of types of training image are provided. An image generation device includes an image acquisition unit configured to acquire a first image; and an image generator configured to generate a training image based on the first image, wherein the first image depicts a first defect included in a first product of a different type the product to be inspected, and the training image is a image to be read by an identifier that identifies whether an inspection image obtained by imaging the product to be inspected depicts a same type of defect as the first defect and to be used by the identifier to perform learning.

Abstract: Methods and systems for determining focus settings for use in a specimen scan are provided. One method includes generating a focus map defined as values of best focus as a function of position on a specimen using output generated in one or more pre-focus swaths scanned on the specimen by an output acquisition subsystem configured to direct energy to a specimen, to detect energy from the specimen, and to generate output responsive to the detected energy. The method also includes interpolating the focus map to generate focus settings for a scan performed on the specimen during a process and storing information for the generated focus settings for use in the scan performed on the specimen during the process.

Abstract: A defect image including a defect and a defect-free image not including a defect for an article different from an inspection article are acquired to teach an identifier when inspecting for a defect in the inspection article. The identifier that has learned the images is made to identify whether an extracted inspection image obtained by segmenting the inspection image of the inspection article includes the defect and the identification results of the identifier are used to determine whether a defect is present in the inspection article. When teaching the identifier the defect, the identifier is provided with, as learning images, a plurality of extracted defect images generated from the defect image by changing an extracting region for extraction from the defect image such that the defect in the defect image is at a different position in each of the plurality of extracted defect images.

Abstract: A method of determining whether a substrate is properly polished includes obtaining an image of the substrate, obtaining intensity values of a luminance plane for the image, generating an intensity histogram from the intensity values of the luminance plane, and analyzing the intensity histogram to determine whether the intensity histogram meets one or more criteria.

Abstract: Automated explanation of machine learning predictions, by: Obtaining an input, a machine learning model, and a classification score produced by application of the machine learning model to the input. Optimizing iterative application of the machine learning model to perturbations of the input, wherein a target of said optimizing is at least a predefined non-zero classification score. Generating, based on one of the perturbations which achieved the predefined non-zero classification score, an explanation to the classification score produced by the application of the machine learning model to the input.

Abstract: The present disclosure provides a system and a method for controlling semiconductor manufacturing equipment. The system includes a sensor, a sensor interface, and an analysis unit. The sensor provides a sensor signal. The sensor interface receives the sensor signal and generates an input signal for a database server. A front-end subsystem receives the input signal from the database server and performs a comparison process to generate a data signal. A calculation subsystem performs an artificial intelligence analytical process to generate an optimal parameter set and a simulated result map according to the data signal. A message and tuning subsystem generates an alert signal and a feedback signal according to the optimal parameter set and the simulated result map, and the message and tuning subsystem transmits the alert message to a user of the semiconductor manufacturing equipment.

Abstract: An image generating part generating feature extraction images by applying an identification part, which has completed learning, that has executed learning in advance to extract features using learning image data to an inspection image, an inspection part specifying an area corresponding to a defect on the basis of judgment parameters for judging presence/absence of a defect in the inspection target object and a binary image generated on the basis of the feature extraction images, and a setting part calculating an image score based on a density of a color of pixels of a setting image using the setting image that is the binary image in which an area corresponding to the defect is specified and updating the judgment parameters such that a difference between an image score of the inside of the area and an image score of the outside of the area becomes relatively large are included.

Abstract: In order to process time series data obtained in a substrate processing apparatus having a plurality of processing units, a data processing method includes an evaluation value calculation step of obtaining a score of the time series data by comparing the time series data with reference data, and a result display step of displaying an evaluation result screen including, for each processing unit, a number of score errors being a number of substrates of which score is abnormal, and a pie chart showing a ratio of the number of score errors to a number of processed substrates. A display size of the number of score errors and that of the pie chart change depending on the number of score errors.

Abstract: Methods and systems include acquiring instances of data relating to multiple layers of a sample obtained via slice and view imaging where the electron interaction depth of the charged particle beam during each irradiation of the sample is larger than the thickness of the first layer and/or the thickness of the second layer. A simulated model is then accessed that identifies a plurality of yield values that identify expected portions/ratios of detected emissions that are expected to be generated by material in corresponding layers/depths of the sample. The yield values are used to segregate the instances of data into component portions based on the particular layer of the sample in which the structures expected to have generated the associated emissions are located. The component portions are then used to create reconstructions of individual layers and/or 3D reconstructions of the sample with reduced depth blur.

Abstract: A substrate processing tool configured for performing integrated substrate processing and substrate metrology, and methods of processing a substrate. The substrate processing tool includes a substrate transfer chamber, a plurality of substrate processing chambers coupled to the substrate transfer chamber, and a substrate metrology module coupled to the substrate transfer chamber. A substrate processing method includes processing a substrate in a first substrate processing chamber of a substrate processing tool, transferring the substrate from the first substrate processing chamber through a substrate transfer chamber to a substrate metrology module in the substrate processing tool, performing metrology on the substrate in the substrate metrology module, transferring the substrate from the substrate metrology module to a second substrate processing chamber through the substrate transfer chamber, and processing the substrate in the second substrate processing chamber.

Abstract: A method for controlling an ultraviolet lamp for even and uniform irradiation of curable glue on a product includes obtaining tilt data of a platform of a machine, where the tilt data comprises a tilt direction and a tilt angle, and calculating a tilt amplitude of the platform of the machine based on the tilt data. The method further includes determining an ultraviolet lamp as a target to be adjusted based on the tilt direction, and obtaining an adjustment range of the target ultraviolet lamp based on the calculated tilt amplitude, and sending the adjustment range to an ultraviolet lamp adjustment device to adjust the target ultraviolet lamp. So that ultraviolet light emitted by the target ultraviolet lamp can illuminate the product to be irradiated on the platform in parallel.

Abstract: Real-time job distribution software architectures for high bandwidth, hybrid processor computation systems for semiconductor inspection and metrology are disclosed. The imaging processing computer architecture can be scalable by changing the number of CPUs and GPUs to meet computing needs. The architecture is defined using a master node and one or more worker nodes to run image processing jobs in parallel for maximum throughput. The master node can receive input image data from a semiconductor wafer or reticle. Jobs based on the input image data are distributed to one of the worker nodes. Each worker node can include at least one CPU and at least one GPU. The image processing job can contain multiple tasks, and each of the tasks can be assigned to one of the CPU or GPU in the worker node using a worker job manager to process the image.

Abstract: A method, performed by a process management apparatus, of managing vision inspection using an artificial intelligence (AI) model and an apparatus therefor are provided. The method includes obtaining first process data related to a first manufacturing process through which a first object passes, identifying a first region on which intensive inspection is to be performed in an entire region of the first object using the AI model and the first process data, controlling a first vision inspector to inspect the identified first region, and determining whether a defect is present in the identified first region.

Abstract: A plurality of captured images is acquired while changing a light illumination state. Each captured image is compared with a corresponding reference image to acquire a region where the captured image is darker than the reference image as a dark defect candidate region. From each of a plurality of captured images, a region where the captured image is lighter than the reference image is acquired as a lightness/darkness inverted region. Among the dark defect candidate regions, those that do not overlap by a prescribed criterion or more with any of the lightness/darkness inverted regions are excluded from defect candidates, and then the presence of a defect is acquired on the basis of the defect candidate regions. This suppresses over-detection of defects arising from, for example, grime on the surface during external appearance inspection.

Abstract: A metrology system may include a characterization tool configured to generate metrology data for a sample based on the interaction of an illumination beam with the sample, and may also include one or more adjustable measurement parameters to control the generation of metrology data. The metrology system may include one or more processors that may receive design data associated with a plurality of regions of interest for measurement, select individualized measurement parameters of the characterization tool for the plurality of regions of interest, and direct the characterization tool to characterize the plurality of regions of interest based on the individualized measurement parameters.

Abstract: There is provided a system and method of generating a training set usable for examination of a semiconductor specimen. The method comprises: obtaining a simulation model capable of simulating effect of a physical process on fabrication process (FP) images depending on the values of parameters of the physical process; applying the simulation model to an image to be augmented for the training set and thereby generating one or more augmented images corresponding to one or more different values of the parameters of the physical process; and including the generated one or more augmented images into the training set. The training set can be usable for examination of the specimen using a trained Deep Neural Network, automated defect review, automated defect classification, automated navigation during the examination, automated segmentation of FP images, automated metrology based on FP images and other examination processes that include machine learning.

Abstract: An inspection apparatus includes: an upstream imaging mechanism that images an upstream wall surface extending from a bottom part of a recess upstream of and adjacent to a projection in a rotating direction of a workpiece to a tip of the projection; and a downstream imaging mechanism that images a downstream wall surface extending from a bottom part of the recess downstream of and adjacent to the projection in the rotating direction to the tip of the projection. At least one of the imaging mechanisms includes a mover for parallelly moving and positioning in an orthogonal plane orthogonal to the axis of symmetry and images the workpiece from a position radially outward of the workpiece and positioned by the mover.

Abstract: Embodiments disclosed herein may comprise receiving a run-time image of a run-time die and, with a deep learning module, identifying a characteristic noise in the run-time image, and modifying the run-time image to reduce the characteristic noise, thereby generating a de-noised run-time image. Such embodiments may be performed as methods, by systems, or from non-transitory computer-readable storage media on one or more computing devices. An image sensor of a metrology tool may capture the run-time image of the run-time die. The metrology tool may include a run-time die disposed on a specimen, a run-time image sensor, and a processor in electronic communication with the run-time image sensor. Embodiments may further comprise receiving a training image of a training die, modifying the training image, and training the deep learning module to identify the characteristic noise in the run-time image and modify the run-time image.

Abstract: Systems and processes for analyzing an image. Analyzing the image may comprise selecting a computer vision parameter for an image feature identification process. The image feature identification process may identify at least one feature in the image when using the computer vision parameter. Analyzing the image may further comprise segmenting the image into a region of interest T and a background region B. Analyzing the image may further comprise calculating a set of statistical values about the region of interest T of the image. Analyzing the image may further comprise classifying the image based on both the computer vision parameter and the set of statistical values as one of either: a defect containing image or a defect free image.

Abstract: According to one embodiment, a retrieval device includes one or more processors configured to retrieve a plurality of search image regions from an intended image through image search using a search query, extract a plurality of learning image regions from a learning image used in learning of the image search, through the image search using the search query, and display the search image regions and the learning image regions on a display.

Abstract: Designs for a hybrid overlay target design that includes a target area with both an imaging-based target and a scatterometry-based target are disclosed. The imaging-based overlay target design can include side-by-side grating structure. A scatterometry-based overlay target design at a different location in the target area can include grating-over-grating structure. A method of measuring the hybrid overlay target design and a system with both an imaging optical system and a scatterometry system for measuring the hybrid overlay target design are also disclosed.

Abstract: Care areas for a first image of a die can be determined according to a 1D offset correction. The 1D offset correction can be based on 1D offsets between the first image and a second image for each of the image frames and also can be based on 1D offsets between a design and the second image for each of the image frames. The care areas can have a zero border for a dimension that is aligned to the design and a legacy border for the other dimension.

Abstract: Systems and methods are provided for generating test patterns. In various embodiments, systems and methods are provided in which machine learning is utilized to generate the test patterns in a manner so that the test patterns conform with design rule check (DRC) specified for a particular semiconductor manufacturing process or for particular types of devices. A test pattern generation system includes test pattern generation circuitry which receives a noise image. The test pattern generation generates a pattern image based on the noise image, and further generates a test pattern based on the pattern image. The test pattern is representative of geometric shapes of an electronic device design layout that is free of design rule check violations.

Abstract: Techniques for secure and tamper-resistant wafer identification using a unique wafer fingerprint are provided. In one aspect, a method for wafer authentication includes: placing, at each level of fabrication of chips on the wafer, reference structures across the chips; inspecting the wafer at each level of the fabrication; and performing at least one of overlay and scatterometry measurements of the reference structures to use as a unique fingerprint for authenticating the wafer that has been inspected. A method for authentication throughout a process flow for fabrication of chips on a wafer is also provided, as is a wafer having chips and reference structures placed across the chips at each level of the chips to provide a unique fingerprint for authenticating the wafer.

Abstract: Designs for a hybrid overlay target design that includes a target area with both an imaging-based target and a scatterometry-based target are disclosed. The imaging-based overlay target design can include side-by-side grating structure. A scatterometry-based overlay target design at a different location in the target area can include grating-over-grating structure. A method of measuring the hybrid overlay target design and a system with both an imaging optical system and a scatterometry system for measuring the hybrid overlay target design are also disclosed.