In one embodiment, a computing system may access design data of a printed circuit board to be produced by a first manufacturing process. The system may analyze the design data of the printed circuit board using a machine-learning model, wherein the machine-learning model is trained based on X-ray inspection data associated with the first manufacturing process. The system may automatically determine one or more corrections for the design data of the printed circuit board based on the analysis result by the machine-learning model.

In one embodiment, a computing system may access design data of a printed circuit board to be produced by a manufacturing process. The system may determine one or more corrections for the design data of the printed circuit board based on one or more correction rules for correcting one or more parameters associated with the printed circuit board. The system may automatically adjust one or more of the parameters associated with the design data of the printed circuit board based on the one or more corrections. The adjusted parameters may be associated with an impedance of the printed circuit board. The one or more corrections may cause the impendence of the printed circuit board to be independent from layer thickness variations of the printed circuit board to be produced by the manufacturing process.

A method of inspecting a structure during additive manufacturing of the structure and additive manufacturing systems are presented. An additive manufacturing system comprises additive manufacturing equipment comprising a casing and an additive manufacturing head configured to form a plurality of layers of a structure within the casing; and an x-ray backscatter imaging system configured to send an x-ray beam into a structure formed within the additive manufacturing equipment and detect scattered x-rays for imaging and analysis of the structure during fabrication.

In one embodiment, an X-ray inspection system may access a first set of X-ray images of one or more first samples that are labeled as being non-conforming. The system may adjust a classification algorithm based on the first set of X-ray images. The classification algorithm may classify samples into conforming or non-conforming categories based on an analysis of corresponding X-ray images. The system may analyze a second set of X-ray images of a number of second samples using the adjusted classification algorithm. The second samples may be previously inspected samples that have been classified as conforming by the classification algorithm during a previous analysis before the classification algorithm is adjusted. The system may identify one or more of the second samples from the second set of X-ray images. Each identified second sample may be classified as non-conforming by the adjusted classification algorithm.

In one embodiment, an X-ray inspection system may nondestructively inspect a printed circuit board to measure a number of dimensions at a number of pre-determined locations of the printed circuit board. The X-ray inspection system may generate a data set for the printed circuit board based on the measured dimensions. The X-ray inspection system may calculate one or more drilling values based on the data set of the printed circuit board. The X-ray inspection system may provide, to a drilling machine, instructions for drilling a number of plated-through vias based on the calculated drilling values for the printed circuit board.

In one embodiment, a computing system may access design data of a printed circuit board to be produced by a first manufacturing process. The system may analyze the design data of the printed circuit board using a machine-learning model, wherein the machine-learning model is trained based on X-ray inspection data associated with the first manufacturing process. The system may automatically determine one or more corrections for the design data of the printed circuit board based on the analysis result by the machine-learning model.

In one embodiment, an automated high-speed X-ray inspection system may identify reference objects for an object of interest to be inspected. Each reference object may have a same type and components as the object of interest. The system may generate a reference model for the object of interest based on X-ray images of the reference objects. The system may determine whether the object of interest is associated with one or more defects by comparing an X-ray image of the object of interest to the reference model. The defects may be characterized by one or more pre-determined defect models and may be classified into respective defect categories based on the pre-determined defect models.

In one embodiment, an X-ray inspection system may capture one or more X-ray images for samples of interest processed by a first tool. The X-ray inspection system may be inline with the first tool and have an inspection speed of 300 mm.sup.2 per minute or greater. The system may determine, in real-time, metrology information related to the samples of interest based on the X-ray images. The metrology information may indicate that a sample parameter associated with the samples of interest is outside of a pre-determined range. The system may provide instructions or data to one or more of the first tool or one or more second tools to adjust process parameters associated with the respective tools based on metrology information. The adjusted process parameters may reduce a processing error probability, of the respective tool for processing subsequent samples, related to the sample parameter being outside of the pre-determined range.

In one embodiment, an automated high-speed X-ray inspection system may generate a first X-ray image of an inspected sample at a first direction substantially orthogonal to a plane of the inspected sample. The first X-ray image may be a high-resolution grayscale image. The system may identify one or more elements of interest of the inspected sample based on the first X-ray image. The first X-ray image may include interfering elements that interfere with the one or more elements of interest in the first X-ray image. The system may determine one or more first features associated with respective elements of interest based on variations of grayscale values in the first X-ray images. The system may determine whether one or more defects are associated with the respective elements of interest based on the one or more first features associated with the element of interest.

An inspection device includes a ray source that irradiates an object to be inspected with energy rays, a detection unit that detects energy rays that have passed through the object to be inspected, a displacement mechanism that sets a relative position of the object to be inspected and the ray source by displacing at least one of the object to be inspected and the ray source in relation to the other, an internal image generation unit that generates an internal image of the object to be inspected based on a detection amount distribution of the energy rays detected by the detection unit, and a control unit that controls the displacement mechanism based on the detection amount distribution of the energy rays detected by the detection unit.