Image analysis – Applications – Manufacturing or product inspection
Reexamination Certificate
1998-06-09
2001-07-31
Mancuso, Joseph (Department: 2623)
Image analysis
Applications
Manufacturing or product inspection
C382S159000, C382S227000, C348S126000
Reexamination Certificate
active
06269179
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to quality control inspection systems and more particularly, to an inspection system and method for bond detection and validation of surface mount devices using vision and infrared sensors, combined with a technique for discriminating and detecting solder joint defects.
BACKGROUND OF THE INVENTION
It is indeed paradoxical that, on one hand, the quality of manufactured printed circuit boards (PCBs) has increased steadily over the past years while, on the other hand, the need for quality assurance has become more pronounced. As packaging technology marches on from through hole to surface mount to multichip modules, the complexity and sophistication of these processes introduce a larger aggregation of defect categories and impose stringent performance requirements The critical role of electronic devices in diverse application areas such as missiles, aircraft, automotive manufacturing, computers and communications, etc. necessitates means for “proof-of-reliability” and metrics of performance. In the case of surface mount technology (SMT), finer pitch leads have increased the complexity of PCBs beyond the point of accurate and repeatable manual inspection. Many studies have been performed that show on a given day, an operator may inspect the same PCB and declare different defects. This lack of reliability and repeatability demands a more accurate approach using automated systems. The most widely used methods for automated inspection are based on vision, infrared (IR),and X-ray. Vision systems are relatively mature but are limited to surface level defects and cannot detect structural defects such as voids. P. J. BesI, et al., “Automated Visual Solder Inspection”,
IEEE Transactions on Pattern Analysis Machine Intelligence
, vol. PAMI-11, pp.42-56, March 1985, D. W. Capson and S- K Eng, “A Tried Color Illumination Approach for Machine Inspection of Solder Joints”,
IEEE Transactions on Pattern Analysis Machine Intelligence
, vol. PAMI-10, pp.387-393, May 1988 and S. L. Bartlett, et al., “Automatic Solder Joint Inspection,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. PAMI-10, pp.32-42, January 1988. Vanzetti used IR for inspection employing the radiance of the material to determine the mass of solder present. This method is not capable of reliably detecting surface level defects such as linear misalignment and angular misalignment. Riccardo Vanzetti and Alan C. Traub, “Combing Soldering with Inspection”, IEEE Control Systems Magazine, pp.29-31, October 1988. The X-ray based inspection uses radiographic imaging techniques to obtain X-ray images of the solder joints for defect classification. M. Juha, “X-ray Machine Vision for Circuit Board Inspection,”
Proceedings of Vision
86
Conf. of SME
, Detroit, Mich., pp.3-41-3-55, June 1986. The X-ray based inspection is reliable in its scope of coverage, but it is very costly and still cannot detect all the defects. The limitations of automated systems are caused by the scope of the individual sensors in classifying the known range of defects. Kenneth Reid, “Automated Soldering Inspection Technology Study,” Proceedings of Technology Program of NEPCON West, Anaheim, Calif., vol. 3, pp.1288-1297, February 1993. The complementary nature of IR and vision sensors improves the reliability of the inspection scheme and broadens the scope of quality assurance strategies. The use of multiple sensors requires a system to fuse the information, process it for classification, and ultimately provide feedback to the process. For the system to have practical application, it should be cost effective, and operate in a timely fashion.
A low-cost, robust, flexible and accurate inspection system for detecting and identifying surface mount defects is needed. Inspection requirements vary from one industry sector to another just as the products to be inspected exhibit an enormous range of variability, however there is no universal definition of a good solder joint. In order to meet such diverse requirements, it was decided to design, develop and implement an inspection system that utilizes the synergism and complementarity of vision and IR sensing in a unified platform that may carry out an inspection task on the basis of either 100% testing or a sampled population. The inspection procedure is performed in two stages in order to reduce the inspection time and to have control over the production line delays. The first stage is called a GROSS inspection station that scans a PCB to determine global features to find probable defective areas of the PCB. The second stage is a FINE inspection station that uses intensive methods on questionable areas and generates local features for classification. Since the FINE inspection is performed off-line, it does not create a bottleneck in the production process. The complementary nature of the vision and IR sensors allows a broad range of classifications that would not be possible using only one sensor. This fusion combined with an on-line and off-line inspection procedure, offers a reliable and time efficient system for solder joint inspection.
SUMMARY OF THE INVENTION
A hybrid inspection system which includes vision and IR sensors. The vision sensors are gray scale charge coupled device (CCD) cameras which take 640-480, 256 gray scale 2-dimensional images of the solder joints. The visual inspection techniques include image processing and pattern recognition approaches for automatic inspection of visible defective joints. The IR side of the system uses a laser to heat the joint and observe the IR radiance curve as the joint heats and cools. The IR radiance curve is called a thermal signature of the joint. A two color IR sensor is used to record the thermal signature of a solder joint. The two color IR sensor is used to get emissivity independent thermal signatures by performing data level sensor fusion.
The GROSS inspection station is employed first on-line, to rapidly detect defects such as missing components, angular misalignment, linear misalignment, and solder mass related defects on a sample basis. A vision system checks for the component's presence and for placement related defects. After these defects have been screened, a statistical sampling scheme is used for IR screening to detect defect categories such as excess solder, insufficient solder, no solder and lifted leads. At this stage, the complete thermal signature of the solder joints is captured, but only the difference between starting and peak value, and the difference between peak and final value is analyzed to determine if it falls within normal range. If a potentially defective board is recognized, the candidate PCB is shipped to the FINE inspection station where another vision system captures a higher resolution two-dimensional 256 gray level image of the probable defective areas and exhaustive defect search is performed at the lead level. For the IR inspection, the complete thermal signature, stored at the GROSS inspection station, is analyzed both in the heating and cooling cycles and the defect, if there is one, is declared and classified. The trade-off here is between speed of inspection and thoroughness. The statistical sampling scheme is used to cut down the time required to insect each joint. The sampling rate can be adjusted to meet the constraints of the process. Sensor fusion techniques have been developed to combine vision and IR sensors' data at various levels of abstraction. A data level sensor fusion methodology for two IR sensors provides emissivity independent thermal signatures of the solder joints. At the FINE inspection station, active perception is employed to perform feature level sensor fusion.
Other features and advantages of the present invention will become apparent to one of skill in the art upon review of the following drawings and the detailed description of the preferred embodiments.
REFERENCES:
patent: 3795918 (1974-03-01), Sunderland
patent: 5115475 (1992-05-01), Lebeau
patent: 5544256 (1996-08-01), Brecher et al.
patent: 5621811 (1997-0
Dar Iqbal M.
Vachtsevanos George J.
Bali Vikkram
Georgia Tech Research Corporation
Mancuso Joseph
Thomas Kayden Horstemeyer & Risley
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