Detectability evaluation of attributes anomaly for electronic components using pulsed thermography

Abstract

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 1 st and 2 nd 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.

Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network

“…For simulating noise during the data capture, each thermal image is added with white noise with different signal-to-noise ratio (SNR) levels. More details about the added noise can be found in our previous work [28]. For Mask R-CNN training, for each defect, the profile image shown in Table 2 is annotated as ground truth into the format of JSON files for ResNet network code convenience.…”

A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure

“…This issue has been captured by illegitimate manufacturers and grey market dealers who are trying to slip through this loophole with the development of counterfeiting methods [8]. X-ray imaging [9] and TeraHertz (THz) testing [10,11] are capable to analyse the accurate internal physical properties such as wires, circuit joint and package material, but cannot be widely used due to either being destructive with low throughput and a high capital cost per inspection unit, or overly sophisticated for unskilled operators [12,13]. Therefore, an intuitive, non-contact, highefficient and low-cost non-destructive inspection technique is highly demanded to deploy the first stage large scale inspection efficiently for reducing the proliferation of ECs with internal structural anomalies.…”