Hideo Okada

Advanced pattern inspection using Macroview

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 (AOI) 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 percent of false alarms.

High-speed autoverifying technology for printed wiring boards

We have developed an automated pattern verification technique. The output of an automated optical inspection system contains many false alarms. Verification is needed to distinguish between minor irregularities and serious defects. In the past, this verification was usually done manually, which led to unsatisfactory product quality. The goal of our new automated verification system is to detect pattern features on surface mount technology boards. In our system, we employ a new illumination method, which uses multiple colors and multiple direction illumination. Images are captured with a CCD camera. We have developed a new algorithm that uses CAD data for both pattern matching and pattern structure determination. This helps to search for patterns around a defect and to examine defect definition rules. These are processed with a high speed workstation and a hard-wired circuits. The system can verify a defect within 1.5 seconds. The verification system was tested in a factory. It verified 1,500 defective samples and detected all significant defects with only a 0.1 percent of error rate (false alarm).