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 method and an apparatus of inspecting a substrate with a component mounted thereon, which are capable of inspecting whether the component is properly mounted or not without additional setting or changing inspection condition, are provided. The method comprises measuring a three-dimensional shape by irradiating the pattern image toward the substrate through at least one illumination unit and by taking a reflected image through an imaging unit, extracting a shield region from the three-dimensional shape, and inspecting a component mounting defect in an area excluding the shield region in the three-dimensional shape.

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.

A computer-implemented method and system provides a labelled training dataset. At least one sub-object or component is selected in a CAD model of an object comprising a plurality of sub-objects or components. A plurality of different render images is generated and the different render images contain the at least one selected sub-object or component. The different render images are labelled on the basis of the CAD model to provide a training dataset based on the labelled render images. Also, a computer-implemented method provides a trained function that is trained on the training dataset. A computer-implemented image recognition method uses such the trained function. An image recognition system comprising an image capture device and a data processing system carries out the image recognition method. A computer program comprises instructions that cause the system to carry out the methods.

An appearance inspection apparatus includes an imaging unit, a reconstructed image generation unit, and an image comparison unit. The imaging unit images an object. The reconstructed image generation unit generates a reconstructed image by using a model, the reconstructed image being an image to be obtained by reconstruction of an input image, image data on the object imaged by the imaging unit being used as the input image, the model being used for attempting to reproduce the image data. The image comparison unit generates a difference image corresponding to a difference between the input image and the reconstructed image.

An electronic component mounting apparatus includes: a transfer unit picking up an upper surface of a light emitting device package having a front surface on which a light emitting diode chip is disposed, and transferring the light emitting device package to a printed circuit board, a light source unit disposed on a transfer path of the light emitting device package, and irradiating measurement light onto the front surface of the light emitting device package, a camera capturing an image of the light emitting device package to which the measurement light is irradiated, and a control unit image-processing the image to identify excitation light, emitted when the measurement light is excited from the light emitting diode chip, in the image, and controlling the transfer unit to mount the light emitting device package on the printed circuit board when identifying the excitation light.

A substrate foreign matter inspection device includes: an image data obtaining device that obtains image data of a target inspection area in the printed circuit board including a printed portion of the solder paste; a storage that stores a neural network and a model, the model being generated by learning of the neural network that includes an encoding portion and a decoding portion by using, as learning data, only image data of the target inspection area that do not include any foreign matter; and a control device that obtains reconfigured image data by inputting original image data obtained by the image data obtaining device into the model, compares the original image data with the reconfigured image data, and determines whether any foreign matter is present or absent on the printed circuit board based on a result of comparison with the reconfigured image data.

In some examples, a system derives a first representation of an assembly of components based on a first source of information, and derives a second representation of the assembly of components based on a second source of information that is of a different type than the first source of information, where the second representation includes any or a combination of an indication of a source of a respective component of the assembly of components, or placement location information of a component in the assembly. The system compares the first representation to the second representation to identify a deviation between the first representation and the second representation, the deviation corresponding to an alteration of the assembly of components.

A system classifies materials utilizing a vision system that implements a machine learning system, such as a neural network, in order to identify or classify each of the materials as either a monetary coin or not a monetary coin, which may then be sorted into separate groups based on such an identification or classification. Such a system can sort monetary coins from other forms of scrap, which may have been produced from a shredding of end of life vehicles.

A mounter is provided with a head unit with a suction nozzle capable of picking up an electronic component that transfers the electronic component to a specified position, an imaging device that images the pickup orientation of the electronic component, a component data acquiring device that acquires the size of the electronic component, an image processing section that image processes the captured image, and an image processing pattern selecting section. The image processing pattern selecting section, based on the size of the electronic component, is able to select one image processing range and one image processing accuracy from multiple predetermined image processing ranges and multiple predetermined printing accuracies, and, as the size of the electronic component acquired from the component data acquiring section becomes smaller, selects a smaller image processing range and a more accurate image processing accuracy from the multiple image processing ranges and multiple image processing accuracies.