In addition, AOI engineers need to cope with several other difficulties a major one of which is
that the production process changes continuously e.g. the settings of devices on the manufac‐
turing line have to be modified, and this needs to be followed also by modifications to the AOI
devices. Therefore the need to monitor the inspection algorithms and adapt to different
parameters is a serious challenge to the process engineers.
Furthermore, it is necessary to satisfy some practical requirements when selecting and
adjusting the inspection algorithms. Usually, electronic factories manufacture more products
in parallel in which several similar or identical components can be located. If all the compo‐
nents were to be inspected with a separate AOI algorithm, the code management, version
tracking and fixing etc. would be impossible. Therefore, engineers often use only one inspec‐
tion method for similar mountings to achieve simpler AOI algorithm version management.
Unfortunately, this strategy cannot always be used successfully because of the very heteroge‐
neous appearance of the same components. Fig.9. shows an image sequence of the C0805
capacitor which illustrates the enormous differences between images taken of similar compo‐
nents.
In this varied environment it is very hard to develop an inspection method which results in
highly reliable classification of each type of image for the same component. In addition, a
parameter setting process that reduces the number of bad classifications in case of one
component influences not only the selected manufacturing line but has an effect on the whole
factory. Therefore it can happen that whilst a parameter optimization process reduces the
number of bad classifications in the first part of the factory, it increases them on other manu‐
facturing lines. This paradox is one of the reasons why the AOI macro optimization process is
a very long and “Sisyphean” task of AOI process engineers.
Figure 26. Differences between the appearances of similar components (capacitor C0805)
A very interesting and important question is the optimization of classification thresholds. One
of the most important requirements of an inspection system is high-level robustness, but this
condition can hardly be guaranteed if the classification decision (namely whether a component
gets “faulty” or “good” label) is dependent on only one pixel. Therefore the quality results
close to the decision threshold need to be classified in a separate group (“limit error”) and it
is necessary to apply a different strategy to them. It follows that AOI experts – apart from the
fact that they need to solve the optimization paradox mentioned earlier – have to strive to find
such an algorithm parameter setting where during the classification, the number of compo‐
nents classified near the decision threshold are as few as possible. Efficiency of AOI appliances
Materials Science - Advanced Topics
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