The invention provides a sample observation system including a scanning electron microscope and a calculator. The calculator: (1) acquires a plurality of images captured by the scanning electron microscope; (2) acquires, from the plurality of images, a learning defect image including a defect portion and a learning reference image not including the defect portion; (3) calculates estimation processing parameters by using the learning defect image and the learning reference image; (4) acquires an inspection defect image including a defect portion; and (5) estimates a pseudo reference image by using the estimation processing parameters and the inspection defect image.

An image inspection device includes: an image acquisition unit to acquire an inspection target image; a geometric transformation processing unit to estimate a geometric transformation parameter for aligning a position of an inspection target in the inspection target image with a first reference image in which a position of the inspection target is known, and geometrically transform the inspection target image using the estimated geometric transformation parameter, thereby generating an aligned image in which the position of the inspection target is aligned with the first reference image; an image restoration processing unit to restore the aligned image, using an image generation network to receive an input image generated using the inspection target image and infer the aligned image as a correct image; and an abnormality determination unit to determine an abnormality of the inspection target using a difference image between the aligned image and the restored aligned image.

Provided are a method and apparatus for detecting a pattern image, and more particularly, a pattern image detection method and a pattern image detection apparatus for effectively detecting a pattern of a template image from a target image. The pattern image detection method and the pattern image detection apparatus can provide an effect of quickly and accurately detecting the pattern of the template image from the target image while reducing the amount of computation for detecting the pattern of the template image.

A sensing and computing system and method for capturing images and data regarding an object and calculating one or more parameters regarding the object using an internal, integrated CPU/GPU. The system comprises an imaging system, including a depth imaging system, color camera, and light source, that capture images of the object and sends data or signals relating to the images to the CPU/GPU, which performs calculations based on those signals/data according to pre-programmed algorithms to determine the parameters. The CPU/GPU and imaging system are contained within a protective housing. The CPU/GPU transmits information regarding the parameters, rather than raw data/signals, to one or more external devices to perform tasks in an industrial environment related to the object imaged.

A method for printing defect detection includes processing and analyzing a difference image obtained by comparing an image scanned with a verifier to a reference image. The detected defects are grouped, and the grouping is refined. Confidence level values are then assigned to the refined groups, and analysis is performed to determine if one or more servicing actions should be taken.

System and methods for matching color and appearance of a target coating are provided herein. The system includes an electronic imaging device configured to receive a target image data of the target coating. The target image data includes target coating features. The system further includes one or more feature extraction algorithms that extracts the target image features from the target image data. The system further includes a machine-learning model that identifies a calculated match sample image from a plurality of sample images utilizing the target image features. The machine-learning model includes pre-specified matching criteria representing the plurality of sample images for identifying the calculated match sample image from the plurality of sample images. The calculated match sample image is utilized for matching color and appearance of the target coating.

One or more images of a portion of a wafer with fabricated devices are acquired using an imaging tool. A pattern of repeating features in an input image of a wafer is identified using various methods, such as correlation and clustering of neighboring vectors. A template is generated based on the found pattern of repeating features. The template is aligned with the acquired image to identify target locations. The target locations are then isolated from the original image for performing detailed metrology.

A method and apparatus for performing automated supervision and inspection of an assembly process. The method is implemented using a computer system. Sensor data is generated at an assembly site using a sensor system positioned relative to the assembly site. A three-dimensional global map for the assembly site and an assembly being built at the assembly site is generated using the sensor data. A current stage of an assembly process for building an assembly at the assembly site is identified using the three-dimensional global map. A context for the current stage is identified. A quality report for the assembly is generated based on the three-dimensional global map and the context for the current stage.

Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

Circuit board inspection by receiving a near infrared (NIR) image of at least a portion of a circuit board, analyzing the NIR image using a machine learning model, and detecting anomalous circuit board portions according to the analysis.