Robotics Automation

Computer Vision for Electronics Manufacturing by L. F. Pau (auth.)

By L. F. Pau (auth.)

DEFECT share OF DETECTION preliminary expense DETECTION cost INSPECTOR three COMPLEXITY OF instances PAN OF appearing o~ ________________________ o~ ______________________ __ -;. INSPECTION activity -;. visible INSPECTION Fagure 1. tendencies in kinfolk among the complexity of inspection projects, illness detection charges (absolute and relative), and inspection time. regardless of the prerequisites defined above, and with the excep­ tion of particular ordinary software platforms (e.g., bare-board PCB inspection, wafer inspection, solder joint inspection, linewidth degree­ ment), imaginative and prescient platforms are nonetheless no longer came across usually in ultra-modern electronics factories. along with price, a few significant purposes for this absence are: 1. The detection robustness or accuracy continues to be inadequate. 2. the full inspection time is frequently too excessive, even though this may usually be attributed to mechanical dealing with or sensing. three. There are power gaps between method engineers, CAD en­ gineers, production engineers, attempt experts, and computing device imaginative and prescient experts, as difficulties dominate the day by day interac­ tions and stop the institution of belief. four. desktop imaginative and prescient experts occasionally nonetheless think that their contributions are common, in order that variation to every actual challenge turns into tedious, or stumbles over the inadequate availabIlity of multidisciplinary services. no matter if we adore it or now not, we needs to nonetheless use acceptable sensors, lighting fixtures, and combina­ tions of algorithms for every type of functions; likewise, we won't layout mechanical dealing with, illumination, and sensing in isolation from each one other.

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Autoregistration reproducibility (see Chapter 15). Data base-to-board registration verification. "Golden board" or reference mask reproducibility (see Algorithm TempI-I). 7. 1). 1. )(n - r)! where n is the defect sample size, r is the number of defects detected in the sample of size n, p is the true fraction of defects in the population of items, and (rln) is the apparent probability of detection. 2. Defect Probability from Area and Image Density It is common to express p, the true fraction of defects in the population of items, by assuming an exponential relation to defect area and density of defects in the image: p = exp(-a· D) Chapter 1 30 Table 5.

See Table 5 for a complete return of investment model, in a specific case. Examples: 1. 1 P1 2. 5 3. 5 Chapter 2 Imaging Microscopes for Microelectronics Various types of microscopes are used in microelectronics. These differ in the illumination fields used (vertical, oblique, darkfield, interferential, differential), light sources, iris and diaphragms, and filters (see Tables 6 and 7). 1. 1. Microscope Lighting For the microscope illumination system, the choice is among the following: 1. Illumination spectra, wideband or single band (laser).

Laser scan microscope (LSM) electronics. ) x 16,000 with, for example, scanning argon ion confocal laser microscopes. The digitized video signal can be processed as a 512 x 512 or 1024 x 1024 pixel image, with a frame repetition period of 2-64 s (Figure 18). 2. Laser Spot Size The laser spot size, d(z), on the sample after beam focusing by a lens must be minimum, meaning that the beam enlargement must be controlled. ROTATING POLYGON TELECENTRIC SCAN LENS SURFACE U DER TEST Figure 17. Raster scanning for 3-D LSM.

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