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TIFAS IR
Thermal Imaging-based Failure Analysis System

  • Category: Failure Analysis
  • Standards: —
  • Target: Hidden flaws
  • Limit: flaw size > flaw depth

TIFAS IR

Thermal Imaging-based Failure Analysis System

We like to judge on first sight. Some aspects, however, may lie hidden below the surface. Good thing, TIFAS IR can help us decide.

As the thermal behavior is often key to the reliability of electronic components, it makes a lot of sense to oberve that behavoir directly to retrieve information about reliability. With the aid of infrared thermography, various defects are detectable in samples, while also being localized and quantified, especially those flaws that directly affect the thermal path. Furthermore, this method is 100% non-destructive and contactless.

With the aid of thermographic methods such as pulse, pulse-phase and lock-in thermography and paired with intelligent image processing, it only takes seconds to reliably evaluate a sample's integrity. The use of a modified flash lamp as excitation source plays an important role to conduct pulse thermography quickly and reliably.

    Versatile failure detection

    TIFAS IR brings laboratory and academic failure analysis down to desktop-scale.

    It is the first all-in-one IR thermography-based failure analysis system and brings everything it takes to failure-analyze electrical components, mechanical parts or joints. Defect sensitivity includes anything that physically obstructs the heat path, which may be (among others):

    • Voids
    • Cracks
    • Delamination
    • Inclusions
    • Foreign objects

    In-line capability

    The joint research project INLINTEST has shown that the technology in TIFAS IR is capable of serving in 100% in-line testing. Its compactness, the short measurement duration and the non-destructive and contactless character of the method allows to include TIFAS IR in production lines for fully-automated quality assessment.

    This comes with additional challenges, like automatic image recognition and a decision-making algorithm that needs training. These are very production line-specific but no big challenges, looking at today's computing power and the emerging of machine learning. Feel free to approach us if you want to learn more.

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