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.

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 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.

A shape measurement apparatus includes a work stage supporting a target substrate, a pattern-projecting section including a light source, a grating part partially transmitting and blocking light generated by the light source to generate a grating image and a projecting lens part making the grating image on a measurement target of the target substrate, an image-capturing section capturing the grating image reflected by the measurement target of the target substrate, and a control section controlling the work stage, the pattern-projecting section and the image-capturing section, calculating a reliability index of the grating image and phases of the grating image, which is corresponding to the measurement target, and inspecting the measurement target by using the reliability index and the phases. Thus, the accuracy of measurement may be enhanced.

In order to reduce inspection tact time in centralized management of a plurality of inspection lines, the system includes: a plurality of inspection lines 4 each having a visual appearance inspection device 3; a first database 5A for storing a captured image captured by each of the visual appearance inspection device 3; and a centralized control device 6 connected to each of the visual appearance inspection devices 3 and the first database 5A, having a display unit 32 that displays an inspection image of an inspection object 7, and enabling visual inspection of the inspection object 7 conveyed on each of the inspection lines 4 in a centralized manner; wherein, while one of the inspection lines 4 is inspected by using the centralized control device 6, the visual appearance inspection devices 3 of the other inspection lines 4 pre-captures the inspection object 7 conveyed on the inspection line 4 under predetermined capturing conditions and stores the pre-captured image in the first database 5A, and wherein the pre-captured image read from the first database 5A is displayed on the display unit 32 in the visual inspection.

The present disclosure proposes an inspection apparatus. The inspection apparatus may include: a structured-light source configured to sequentially radiate a plurality of structured lights having one phase range; a lens configured to adjust, for each of the plurality of structured lights, optical paths of light beams corresponding to phases of the phase range such that a light beam corresponding to one phase of the phase range arrives at each point of a partial region on an object; an image sensor configured to capture a plurality of reflected lights generated by the structured lights being reflected from the partial region; and a processor configured to acquire a light quantity value of the reflected lights; and derive an angle of the surface by deriving phase values of the reflected lights based on the light quantity value for the reflected lights.

A circuit board detection method includes obtaining an input circuit board image, performing a detection on designated components of a circuit board in the circuit board image according to a preset detection method, determining whether a designated component in the circuit board image that fails the detection is allowed to shift within a preset angle range, and determining that the circuit board passes the detection when the designated component that fails the detection is allowed to shift within the preset angle range. The designated components include one or both of silkscreened components and non-silkscreened components.

A printed circuit board inspection apparatus can inspect the mounting state of a component by generating depth information on the component mounted on a printed circuit board by using a pattern of light reflected from the component and received by an image sensor, inputting the generated depth information into a machine-learning-based model, obtaining depth information with reduced noise from the machine-learning-based model, and using depth information with reduced noise.

One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.

In one embodiment, a method includes inspecting a fiber weave for use in a printed circuit board with an automated optical inspection tool and identifying a distance between fiber bundles in the fiber weave. The fiber weave comprises a plurality of the fiber bundles woven to form the fiber weave and a portion of the fiber bundles comprise markers and identifying a distance between the fiber bundles in the fiber weave comprises measuring a distance between the markers.