This application discloses a video inspection system for a rework station, which includes multiple image capture devices to capture multiple images or videos of a printed circuit board assembly, a presentation tool to merge the captured images or video into a image or video, and a display device to present the image video. The presentation tool also can augment the captured video of the printed circuit board assembly with information from a layout design of the printed circuit board assembly. The presentation tool can receive a selection of a portion of the layout design or a selection of at least one component in the printed circuit board assembly, and annotate the captured video of the printed circuit board assembly with design data from the layout design that corresponds to the selected portion of the layout design or the selected component in the printed circuit board assembly.

An image-recognition method is provided. The method includes the following steps: receiving a plurality of check-point images, and classifying the check-point images into a plurality of groups; classifying the check-point images in each group into a plurality of types to generate first structured data; balancing a number of the check-point images in each type of each group in the first structured data to generate second structured data, wherein the second structured data includes training-set data and testing-set data; training an artificial-intelligence (AI) model using the training-set data; and inputting the testing-set data into the AI model to obtain a model evaluation of the AI model.

An exemplary computer vision system implemented by a multi-access edge computing (“MEC”) server integrated within a provider network accesses an image captured by an image capture device coupled with the MEC server via the provider network. The image depicts a product that is to comply with a predefined product standard. The computer vision system identifies a criterion that is associated with the predefined product standard, and a region of the image that is associated with a product condition that is to be verified for compliance with the criterion. The computer vision system determines whether the product condition complies with the identified criterion for the identified region of the image, and, based on this determination, generates compliance verification data indicative of whether the product complies with the predefined product standard. The computer vision system also provides the compliance verification data to a monitoring system separate from the MEC server.

In some embodiments, methods include acquiring a micrograph image of a plated through hole and converting the micrograph image to a binary image. Methods include defining a pixel line at a copper-dielectric material interface of the binary image. In some embodiments, methods include comparing a length of an interface line compared to a length of a portion to determine a roughness of the pixel line. In some embodiments, methods include determining a roughness of the hole wall before copper plating. Methods may include determining a roughness of the interface using the pixel line.

An inspection system that includes a memory; a configurable acquisition channel; a controller that is adapted to: (a) determine, in response to a first frequency wise relationship between the first noise power spectrum and the first signal power spectrum, a first configuration of the configurable acquisition channel; and (b) determine, in response to a second frequency wise relationship between the second noise power spectrum and the second signal power spectrum, a second configuration of the configurable acquisition channel; and wherein the configurable acquisition channel is adapted to: (a) acquire the image of the first area of the inspected object while being configured according to the first area configuration, and (b) acquire the image of the second area while being configured according to the second area configuration.

An inspection system includes a memory; and a processor, wherein the memory is configured to hold an image reproducing model trained to reproduce, from a first masked image generated by masking part, of a first image determined as including no defect from among images that capture an object to be inspected, the first image before being masked, and wherein the processor is configured to determine, based on a reproduced image reproduced by inputting a second masked image generated by masking part of a second image that captures a new object to be inspected into the image reproducing model, and the second image, whether the second image includes a defect.

The present disclosure provides a substrate, an alignment method and an alignment device. The substrate includes an alignment mark and an auxiliary alignment mark. A shape of the auxiliary alignment mark is different from a shape of the substrate alignment mark.

The integrated circuit assembly can include: a semiconductor and a substrate (e.g., PCB). The integrated circuit assembly can optionally include: a compliant connector, a thermal management, and a securing element. The semiconductor 210 can include a first alignment feature. (e.g., orifice). The substrate can include a second alignment feature (e.g., alignment target) and conductive pads. The substrate can optionally include a cavity.

Embodiments consistent with the disclosure herein include methods for image enhancement for a multi-beam charged-particle inspection system. Systems and methods consistent with the present disclosure include analyzing signal information representative of first and second images, wherein the first image is associated with a first beam of a set of beams and the second image is associated with a second beam of the set of beams; detecting, based on the analysis, disturbances in positioning of the first and second beams in relation to a sample; obtaining an image of the sample using the signal information of the first and second beams; and correcting the image of the sample using the identified disturbances.

One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.