A drill hole inspection method for using a drill hole inspection system to inspect a drill hole of a printed circuit board (PCB) is provided. The drill hole inspection method includes: scanning the PCB to obtain 3-dimensional (3D) image voxel data of the PCB; determining a first-dimension interval corresponding to the PCB along one direction of the 3D image voxel data; determining, in a plane of the 3D image voxel data which is orthogonal to the direction, a 2-dimensional (2D) position corresponding to the drill hole; extracting a portion of the 3D image voxel data as drill hole voxel data according to the first-dimension interval and the 2D position; analyzing the drill hole voxel data to obtain an inspection result of the drill hole; and outputting the inspection result through an output component. In addition, a drill hole inspection system and an inspection device using the same are also provided.

A board inspection apparatus is disclosed, which includes a surface-side irradiator irradiating a surface of each inspection area of a board, a surface-side camera taking a surface-side image of the surface, a rear face-side irradiator irradiating a rear face of each inspection area, a rear face-side camera taking a rear face-side image of the rear face, and a controller moving the surface-side irradiator and the surface-side camera to a position corresponding to the surface of each inspection area, moving the rear face-side irradiator and the rear face-side camera to a position corresponding to the rear face of each inspection area, and inspecting the surface and the rear face of each inspection area based on the surface-side image and the rear face-side image, respectively.

A dispensing system for depositing material on an electronic substrate includes a frame, a dispensing unit gantry movably coupled to the frame, a dispensing unit coupled to the dispensing unit gantry, a vision system gantry coupled to the frame, and a vision system coupled to the vision system gantry. A controller is configured to manipulate the vision system with the vision gantry system to move to the position defined by a feature, to acquire an image of at least a portion of a feature, to search for an edge of interest along a center of the image, and to return a value indicating an offset of zero (0), which is interpreted as the location that is exactly as expected, and an offset that reflects where the edge of interest intersected that axis location.

Provided is an image processing system capable of estimating a three-dimensional shape of a semiconductor pattern or a particle by solving problems of measurement reduction in a height direction and taking an enormous amount of time at a time of acquiring learning data. The image processing system according to the disclosure stores a detectable range of a detector provided in a charged particle beam device in a storage device in advance, generates a simulated image of a three-dimensional shape pattern using the detectable range, and learns a relationship between the simulated image and the three-dimensional shape pattern in advance.

Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

A printed circuit board inspection apparatus can inspect a mounting state of a component by generating depth information on the component by using a pattern of light reflected from the component mounted on a printed circuit board received by an image sensor, generating two-dimensional image data for the component by using at least one of light of a first wavelength, light of a second wavelength, light of a third wavelength, and light of a fourth wavelength reflected from the component received by a first image sensor, inputting the depth information and the two-dimensional image data for the component into a machine learning-based model, obtaining depth information with reduced noise from the machine learning-based model, and using the noise-reduced information.

The present disclosure provides a method including providing a first image and a second image. The first image is of a substrate having a defect and the second image is of a reference substrate. A difference between the first image and the second image is determined. A simulation model is used to generate a simulation curve corresponding to the difference and the substrate dispositioned based on the simulation curve. In another embodiment, the scan of a substrate is used to generate a statistical process control chart.

This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

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.

An operation assistance device includes a processor that executes a procedure. The procedure includes acquiring an operation image captured at each unit of operation in a linking operation to link a plurality of devices together, identifying positions, in the acquired operation image, of connection portions respectively provided at the plurality of devices, based on marker information included in the operation image, and based on design information of the plurality of devices, the design information including position information and a linkage state of the connection portions, executing image analysis processing on the identified positions of the connection portions to determine state of each connections made at each of the units of operation, based on a result of comparing a design image generated based on the design information and the operation image, and outputting information indicating a determination result to a display section.