A screen mask inspection device inspects a screen mask including a screen opening that forms a printing pattern, and includes: an inspection control device that detects solder position information of a solder paste printed on a substrate via the screen opening, and based on the solder position information, determines whether a quality of printing using the screen mask is good or bad, the solder position information being based on an amount of positional misalignment of the solder paste actually printed on the substrate relative to a predetermined reference position.

A system for inspecting a reflective surface includes a first imaging assembly configured to take a first image of the reflective surface, the first image including depth information, a second imaging assembly configured to take a second image of the reflective surface, the second image including contrast information, and a processor configured to acquire the first image and the second image. The processor is configured to perform: estimating a depth profile of the surface based on the depth information, correlating the depth profile with the second image, and identifying a feature of the reflective surface based on the correlation.

A substrate inspection apparatus generates, when anomalies of a plurality of second solder pastes among a plurality of first solder pastes printed on a first substrate is detected, at least one image indicating a plurality of second solder pastes with anomalies detected by using an image about a first substrate, applies the at least one image to a machine-learning-based model, acquires a plurality of first values indicating relevance of respective first fault types to the at least one image and a plurality of first images indicating regions associated with one of a plurality of first fault types, determines a plurality of second fault types, which are associated with the plurality of second solder pastes by using the plurality of first values and the plurality of first images, and determines at least one third solder paste, which is associated with the respective second fault types.

A printing solder point quality identification and maintenance suggestion system and a method thereof are disclosed. In the system, when solder paste inspection data, component inspection data or circuit inspection data indicates presence of defect, an analysis and calculation device intercepts the operating data, the equipment data, the raw material data, the process data and the environment data from a time data stream, to generate a data feature portrait. The analysis and calculation device then sets a reliability value based on location association between the solder paste inspection data, the component inspection data, the circuit inspection data and the maintenance data in the location data stream, and performs similarity calculation on the data feature portrait and the comparison data feature portrait to calculate a similarity value, and then calculates a relative reliability value, and compares the relative reliability value with a reliability threshold value to generate maintenance suggestion information.

An object is to shorten time required for visual inspection. A visual inspection device (1) configured to inspect an appearance of an inspection object (30), the visual inspection device (1) including: an imager (3) configured to image the inspection object (30) arranged at a predetermined position of the visual inspection device (1); and a height measurer (11, 12) configured to measure a height of the inspection object (30) carried into the visual inspection device (1) or carried out from the visual inspection device (1).

Provided is a technique capable of more accurately determining a solder protruding defect in an appearance inspection device that acquires an image of an inspection region of an inspection target and measures a height of a predetermined place in the inspection region with a height measurement device. The appearance inspection device includes: an imaging unit (3); a height measurement unit (20); a moving mechanism (5) that moves the imaging unit (3) and the height measurement unit (20). When a restricted region (M) in the inspection target is irradiated with the measurement light emitted from the height measurement unit (20), the determination unit restricts defect determination based on the information on the height of the predetermined place measured by the height measurement unit (20).

The present disclosure provides an inspection device for use in a mounting system including a mounting device for disposing a component on a board, including a control section configured to extract a mass area included in a captured image resulting from imaging a processing target object where a viscous fluid is formed at a predetermined part, obtain a center of gravity of the mass area so extracted, and determine whether the center of gravity is included in a normal range of the predetermined part as a reference of the captured image to thereby determine whether a bridge has occurred where the viscous fluid is formed over adjacent predetermined parts.

A system and a method for assessing reliability of an electronic component. The method may include training a machine earning (ML) algorithm and/or a classification network to classify electronic components based on one or more features, attributes or characteristics related to reliability of the electronic components, e.g., related to a level of solderability of the components lead or balls or features indicating of tampering of the electronic component. By receiving an image of a test electronic component and extracting a feature related to reliability of the test electronic component from the image received, embodiments of the invention may enable classifying the test electronic component to a class indicating a reliability of the test electronic component by using the machine learning algorithm and/or the classification network.

A training device and a training method for a neural network model. The training method includes: obtaining a data set; completing, according to the data set, a plurality of artificial intelligence (AI) model trainings to generate a plurality of models corresponding to the plurality of AI model trainings respectively; selecting, according to a first constraint, a first model set from the plurality of models; and selecting, according to a second constraint, the neural network model from the first model set.

A shape measuring device includes a light source unit, a light splitting unit, a reference mirror, a light receiving unit and a processing unit. The light source unit generates light and can change the wavelength of the light. The light splitting unit splits the light generated from the light source unit into at least a reference light and a measurement light. The light receiving unit receives the reference light which is reflected by the reference mirror so as to form a reference light path, and the measurement light which is reflected by a light-transmitting target object formed on a substrate so as to form a measurement light path. The processing unit calculates the shape of the measurement argent object based on an interference change resulting from a wavelength change of the light between the reference light and the measurement light received by the light receiving unit.