This paper describes a 3 -D laser scanning imager for visual inspection of mounted devices on printed circuit boards (PCB). A 3 -D imager for this application must satisfy the following requirements: (1) It must be fast enough to sense a 250 by 330 mm area in 14 seconds; (2) It must have a measurement resolution of at least 125 Fun; (3) It must be capable of measuring height and light intensity simultaneously; and (4) It must have an optical dynamic range of at least 104.We developed a wide -area telecentric scanning optical system which meets these requirements. It uses retroreflective triangulation optics and digital signal processing hardware.Our system scans a laser beam over a 256 mm length with a resolution of 125 Fun, without scanning distortion. The retroreflection triangulation optics collect light reflected from objects on a printed circuit board and focus the image on a position-sensitive detector (PSD). This system measures the profile of objects with a vertical resolution of 30 im, within a range of 7.6 mm.The digital signal processing hardware has a dynamic range of 104 and obtains range data from the output signals of the PSD. Its processing speed is 1M pixels /s. This hardware enables profile measurement of objects having a wide range of light reflectance (about 3000 times), from black devices to glossy metal, with an accuracy of 0.1 mm.This 3 -D imager was used in an automated inspection system for PC board-mounted devices. This system detects missing, misplaced, and incorrectly installed devices with an inspection speed of 0.1 s/device.
The new pattern inspection algorithm we developed detects fatal defects on printed wiring boards. The algorithm determines whether patterns identified by an automated pattern inspection (AOl) system are actually defective by considering the electrical malfunction that the defect will cause. A macroscopic model based on the pattern design rules and their tolerances to pattern violations is needed to evaluate defects. The algorithm classifies features around a defective pattern into 50 categories and compares the defect distribution with preset check rules. The automated optical verification system we developed captures pattern images with a CCD camera and uses verification software to evaluate defects. The process takes 10 seconds per image. We tested the system on the factory floor, and it detected all defects with less than 4.8% of false alarms.
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