The Leading-Edge Technology for Automatic Defect Detection
SPEA Automatic Test Equipment is among the forerunners in the intelligent assessment of electronics' failures
Automatic testing combined with artificial intelligence (AI) is a key technology that opens new opportunities for detecting defects and delivering higher-quality products with increased efficiency.
Pioneer in advanced automatic test equipment, SPEA brings the power of artificial intelligence (AI) to the automatic testing to increase defect detection coverage and accuracy.
Defects of electronic products come in all shapes
Defects of electronic products can occur due to issues such as weak design, inaccurate wiring, wrong placement, aesthetic variations due to manufacturing cycle operations and more.
These can be assessed with a set of testing techniques including electrical, optical, mechanical, thermal, and geometrical tests.
Timely defect detection, through automatic test equipment able to combine all these testing techniques, allows manufacturers to continuously improve their production standards as well as avoid cost implications and reputation damage.
Considering the increased volume of new products and electronics’ shorter life-cycle, having automatic test equipment capable of enhancing traditional testing techniques and supporting the production process efficiently is definitely a major competitive advantage.
Beyond the traditional automatic defect detection
Detecting electrical components’ functionality may be complex for some manufacturing environments and traditional testing techniques may present some weaknesses:
Unexpected conditions can not be programmed
Defect detection must be programmed accordingly, thus every component that gets inspected necessitates test requirements designed with specific rules. This means that only known issues can be inspected because they are so programmed.
Misjudgment is around the corner
As traditional automatic tests don’t emulate human inspection and there is no real judging intelligence when, for example, pins are not tilted in the same way or there are light reflections that change the surface color, the chances of misjudgment and false defects can happen.
Intolerance to variability is high
An automatic test program is locked: if there are product design changes it is necessary to adjust acceptance criteria settings or the tester may not be able to judge. Whenever a new product is used in the industry, new algorithms must be developed to detect it.
Artificial Intelligence (AI) technology can be particularly accurate and effective to cover the weaknesses of traditional automatic testing techniques.
By applying self-learning capabilities to automatic testers for defect detection, it can reduce the false detection rate and further increase the production rate.
How AI combined with ATE works to capture defects
Artificial Intelligence (AI) is the innovative technology learning to identify unconventional conditions and increase the capacity of the traditional optical test. It uses automatic test equipment’s vision units to analyze product images and find specifics in shape, dimensions, and color, just to name a few.
The real power of Artificial Intelligence (AI) is deep learning, which uses fast neural network algorithms that mimic the human brain to recognize images, learn complex patterns in data and provide real-time detection.
Labeled images of various types of components with and without defects train the neural network.
The network learns to recognize different images and automatically identifies unexpected conditions due to dynamic properties. The more entries are loaded in the network, the more it will be able to learn.
The automatic recognition of components mounted on PCB and the assessment of EV battery storage rivet nut position were successfully performed, using advanced optical testing combined with artificial intelligence, to identify unexpected conditions that could have created inaccuracies and product defects.
Closely collaborating with experts in the AI field and Universities, SPEA is setting a new milestone in defect detection bringing the power of AI to electronics manufacturers.
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