Abstract: An optical inspection system and method for inspecting a component on a printed circuit board (PCB) which determines three-dimensional information in a single scan. A first visual light source illuminates the PCB surface and component with a green light while a second visual light source illuminates the PCB and component with a blue light. At least one laser light source simultaneously illuminates the surface of the PCB with a narrow coherent red-light laser beam. The laser light source is mounted off vertical on a movable mount which enables the laser beam to be directed over an area of interest on the surface of the PCB. The system also includes a color scan camera mounted vertically above the PCB. The camera has red, green, and blue channels. The green and blue channels capture an image of the illuminated surface of the PCB which is used by a computer to determine two-dimensional information about the component.

Abstract: A system and method allow for associating a descriptive label with an anomaly on a manufactured object, such as a semiconductor wafer. The method includes placing the manufactured device on a moveable stage; capturing and preparing a digital-pixel-based representation of the image; symbolically decomposing the digital-pixel-based representation of the image to create a primitive-based representation of the image; analyzing the primitive-based representation of the image to detect and locate the anomaly; isolating primitives associated with the anomaly; comparing the isolated primitives associated with the anomaly with primitives in a knowledge base to locate a set of primitives in the knowledge base most like the isolated primitives associated with the anomaly; and assigning a label associated with the set of primitives in the knowledge base that was most similar to the isolated primitives associated with the anomaly.

Abstract: An apparatus, system, and method of optically inspecting printed circuit boards (PCBs) for defects, that reliably determines the dimensions of components including those having the same color as the background, and which can detect components which are missing, misoriented, misaligned, or not properly seated. The apparatus uses a camera and a coherent primary light source mounted at an angle away from the vertical so as to produce sharply defined PCB component shadows on the top surface of the PCB. An image of the PCB is captured, the shadow edges are symbolically decomposed into primitives from which gradients are produced, and then compared to a previously captured gradient of a defect-free PCB. Differences in the two image gradients, if any, are used to identify missing, misaligned, misoriented, and improperly seated components, and to detect foreign objects and other PCB defects.

Abstract: A system and method for repairing a defect on a manufactured object, which may be a semiconductor wafer, uses a computer and a repair tool.

Abstract: A system and method for detecting anomalies on a manufactured device includes or utilizes a moveable stage for holding and positioning the device, a camera for capturing an image of the device on the stage, a digitizer coupled to the camera for producing a digital-pixel-based representation of the image, and a computer having a processor and memory. The computer is coupled to the digitizer for receiving the digital-pixel-based representation from the digitizer and coupled to the stage for selectively moving the stage to align the device. The computer is programmed to be operable to symbolically decompose a digital-pixel-based representation of an image to create a primitive-based representation of the image. The image may be aligned by the computer with respect to the rotation of geometric objects in the image by developing a histogram of angles and lengths and matching them to determine a rotational shift.

Abstract: An apparatus and method for automatic knowledge-based object or anomaly classification is provided by capturing a pixel map of an image and from that generating high level descriptors of the object or anomaly such as size, shape, color and sharpness. These descriptors are compared with sets of descriptors in a knowledge-base to classify the object or anomaly.