Browsing by Author "Liu, Xiaochen"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Detectability evaluation of attributes anomaly for electronic components using pulsed thermography
    (Elsevier, 2020-09-16) Liu, Haochen; Tinsley, Lawrence; Addepalli, Sri; Liu, Xiaochen; Starr, Andrew; Zhao, Yifan
    Counterfeit Electronic Components (CECs) pose a serious threat to all intellectual properties and bring fatal failure to the key industrial systems. This paper initiates the exploration of the prospect of CEC detection using pulsed thermography (PT) by proposing a detectability evaluation method for material and structural anomalies in CECs. Firstly, a numerical Finite Element Modelling (FEM) simulation approach of CEC detection using PT was established to predict the thermal response of electronic components under the heat excitation. Then, by experimental validation, FEM simulates multiple models with attribute deviations in mould compound conductivity, mould compound volumetric heat capacity and die size respectively considering experimental noise. Secondly, based on principal components analysis (PCA), the gradients of the 1st and 2nd principal components are extracted and identified as two promising classification features of distinguishing the deviation models. Thirdly, a supervised machine learning-based method was applied to classify the features to identify the range of detectability. By defining the 90% of classification accuracy as the detectable threshold, the detectability ranges of deviation in three attributes have been quantitively evaluated respectively. The promising results suggest that PT can act as a concise, operable and cost-efficient tool for CECs screening which has the potential to be embedded in the initial large scale screening stage for anti-counterfeit.
  • Thumbnail Image
    ItemOpen Access
    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