Browsing by Author "Lam, Wayne"

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    Inspection of electronic component using pulsed thermography
    (Elsevier, 2021-07-14) Tinsley, Lawrence; Liu, Haochen; Addepalli, Sri; Lam, Wayne; Zhao, Yifan
    Counterfeit electronic components (CEC) are of great concern to governments and industry globally as they could lead to systems and mission failure, malfunctioning of safety critical systems, and reduced reliability of high-hazard assets. Depending on the cost of CEC going into the production line, some industries might look to have some sort of inspection capability in-house to screen critical components before they go to assembly. Although advanced counterfeit inspection methods have been developed for a variety of components, they generally exhibit a combination of disadvantages such as destructive, low throughput, high unit cost, or inaccessible to unskilled operator. This paper investigates the potential of pulsed thermography on detection of CEC in a fast and non-destructive manner. The second derivative of post-heat thermal response is used to construct a fingerprint to differentiate genuine and counterfeit components. Results successfully demonstrate the potential of the proposed solution.
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    A novel inspection technique for electronic components using thermography (NITECT)
    (MDPI, 2020-09-03) Liu, Haochen; Tinsley, Lawrence; Lam, Wayne; Addepalli, Sri; Liu, Xiaochen; Starr, Andrew; Zhao, Yifan
    Unverified or counterfeited electronic components pose a big threat globally because they could lead to malfunction of safety-critical systems and reduced reliability of high-hazard assets. The current inspection techniques are either expensive or slow, which becomes the bottleneck of large volume inspection. As a complement of the existing inspection capabilities, a pulsed thermography-based screening technique is proposed in this paper using a digital twin methodology. A FEM-based simulation unit is initially developed to simulate the internal structure of electronic components with deviations of multiple physical properties, informed by X-ray data, along with its thermal behaviour under exposure to instantaneous heat. A dedicated physical inspection unit is then integrated to verify the simulation unit and further improve the simulation by taking account of various uncertainties caused by equipment and samples. Principle component analysis is used for feature extraction, and then a set of machine learning-based classifiers are employed for quantitative classification. Evaluation results of 17 chips from different sources successfully demonstrate the effectiveness of the proposed technique