processes can only be used to a limited degree because of the high-complex manufacturing
process and the equally complex and highly varied appearance of devices under test (DUTs).
This contradiction invokes the conclusion that accuracy and reliability of AOI system depends
very much on the competence and working quality of the engineers and operators, the correct
management of the setting up and controlling the inspection devices. In reality, this sets out
several very serious challenges to experts. The quality inspection algorithms have many
parameters – in some cases several hundred – (image processing, region of interest, threshold
parameters etc.). Their setup requires experience, intuition and inspiration from the process
engineers themselves.
In addition, during parameter tuning, the engineers need to solve the following contradiction,
where the difference between images showing correct and faulty components is often only a
few pixels which need to be detected by the AOI devices (Fig. 8). In the case of incorrect
parameter settings these small signals can disappear and the system classify a bad component
as good (“slip-through”). Certainly this false classification is totally intolerable in quality
inspection processes; therefore it is necessary to aim for the complete elimination of this
possibility by fine tuning the algorithm’s parameters. Unfortunately because of this, engineers
can easily set the algorithm to be too strict, meaning also that some correct components will
be dropped out during the inspection process. Although these “false calls” (also known as
pseudo failure) do not cause catastrophic consequences nevertheless they are the source of a
very serious problem. Namely, in this instance, the human operators performing the re-
inspection of components considered “faulty” can easily get used to the repeated mistakes of
the AOI system. Therefore they can eventually take the inspection device’s decisions out of
consideration even where there is a cases of real errors. This implies that the reliability of the
inspection device itself would be in doubt; the fact of which would result in one of the biggest
catastrophic effects on AOI systems. In addition, it seems insignificant but it is important to
note that many bad classifications slow the manufacturing process, decrease productivity and
increase the product overall production costs. To avoid false calls, process-engineers need to
reduce the strictness of the inspection parameters which – as we have mentioned earlier – is
inconsistent with principle used by the parameter settings preventing the slip-through.
Figure 25. An example for the tiny differences between the images containing correct and faulty components
Automatic Optical Inspection of Soldering
http://dx.doi.org/10.5772/51699
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