A computer-implemented method of automated X-ray inspection during the production of printed circuit board, PCB, assemblies. The method includes capturing an X-ray image of a PCB assembly, determining a first error indicator based on image processing of the captured X-ray image, determining, in case the first error indicator indicates the PCB assembly as faulty, a second error indicator based on the captured X-ray image using a trained adaptive algorithm, and outputting the second error indicator as a result of the inspection.

A material handling system sorts materials utilizing a vision system of multiple vision devices configured with single board computers that each implement an artificial intelligence system in order to identify or classify materials, which are then sorted into separate groups based on such an identification or classification by sorting devices that are each coupled to one of the vision devices.

An image positioning system based on upsampling and a method thereof are provided. The image positioning method based on upsampling is to fetch a region image covering a target from a wide region image, determine a rough position of the target, execute an upsampling process on the region image based on neural network data model for obtaining a super-resolution region image, map the rough position onto the super-resolution region image, and analyze the super-resolution region image for determining a precise position of the target. The present disclosed example can significantly improve the efficiency of positioning and effectively reduce the required cost of hardware.

Some example embodiments relate to a super resolution scanning electron microscope (SEM) image implementing device and/or a method thereof. Provided a super resolution scanning electron microscope (SEM) image implementing device comprising a processor configured to crop a low resolution SEM image to generate a first cropped image and a second cropped image, to upscale the first cropped image and the second cropped image to generate a first upscaled image and a second upscaled image, and to cancel noise from the first upscaled image and the second upscaled image to generate a first noise canceled image and a second noise canceled image.

A mounting device mounts components on a board, and performs processing of arranging discard components discarded based on a captured image on a discard loading section. A CPU of a management computer acquires an identification image in which it is possible to identify a discard component to be discarded and a captured image of the discard component, links the acquired identification image of the discard component and the captured image of the discard component, and creates a discard component arrangement image screen that includes an arrangement display area in which the identification images are arranged based on an order in which the discard components were arranged on discard loading section. The CPU of management computer then outputs the created discard component arrangement image screen.

A system and a method for tracing components of an electronic assembly. The method may include obtaining an image of a component of the electronic assembly, generating identification information of the component based on visual features of the component, authenticating the component based on the identification information, generating correlating information by associating the component to an electronic assembly and classifying the component to a group based on the visual features of the component.

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.

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.

A solder printing inspection device includes: an illumination device that irradiates, with a predetermined light, a printed circuit board on which a solder paste is printed; an imaging device that takes an image of the printed circuit board irradiated with the predetermined light and obtains image data; and a control device that: based on the image data, obtain three-dimensional measurement data of the solder paste printed on the printed circuit board, based on the three-dimensional measurement data, extracts upper portion shape data of an upper portion of the solder paste, the upper portion having a height equal to or higher than a predetermined height, and compares the upper portion shape data with a predetermined criterion and determines whether a quality of a three-dimensional shape of the upper portion of the solder paste is good or poor.

Provided is a method of measuring uniformity of a signal line pattern formed on a printed circuit board. The method includes acquiring, by a microscope camera, an image by photographing straight-line shaped dummy signal line patterns formed side by side in a lateral or longitudinal direction on a surface of the outer-layer board, receiving, by a processor included in computing equipment, the image from the microscope camera and marking a first virtual line and a second virtual line, which cross the dummy signal line patterns in the longitudinal direction, on the image, generating, by a user interface included in the computing equipment, intersection points between the first and second virtual lines and the dummy signal line patterns on the image according to manipulation of a measurer, calculating, by the processor, a first distance value between adjacent intersection points located on the first virtual line and a second distance value between adjacent intersection points located on the second virtual line, and measuring, by the processor, uniformity of the signal line pattern on the basis of a difference value between the first distance value and the second distance value.