Towards development of an intelligent failure analysis system based on infrared thermography. (December 2022) - Pareek, Kaushal Arun; May, Daniel; Ras, Mohamad Abo; Wunderle, Bernhard
Abstract: Electronic components of which reliability cannot be quantified are unacceptable and potentially hazardous, especially in safety-relevant areas such as driver assistance systems and medical technology where the zero-error principle applies. Reliability as a quality criterion has its origin in production, i.e. process variations have a negative influence on the structural integrity of the contact elements on the packaging and interconnect technology and, thus on the device performance in the field under thermo-mechanical load (temperature changes, vibration, humidity). At present, to ensure reproducibility of the reliability of each component, regular quality tests are often carried out in practice. However, a better and reliable approach will carry out 100 % inline checks for traceability and immediate readjustment. This work is the first step towards developing an intelligent non-destructive inline-capable failure analysis technique using infrared thermography. Good data forms the base on which robust and accurate AI algorithms can be trained and developed. However, the obtained thermographic images need to be processed so that subsurface defects can be detected. In this work, prominent algorithms, namely Pulse Phase Thermography (PPT), Thermographic Signal Reconstruction (TSR), Principal Component Thermography (PCT), Slope and Correlation Coefficient, have been thoroughly discussed and examined on the thermographic sequence from a plexiglass sample. A hybrid algorithm of TSR and PCT has also been suggested with promising results. In the end, potential post-processing algorithms from which the obtained results can be used for training an ML/AI model have been discussed. The major problem associated with the deep learning approach is the lack of data in the training set. The model's performance is dependent on the quality and quantity of the training data. The training data should be a good representation of the real-world scenario, i.e. it should be accurate enough and contain enough images, including exceptions for learning reliable features. However, getting sufficient data is a challenge in the manufacturing industry. However, using the Finite Element Model (FEM) approach for data generation can help overcome the data hurdle. In this paper, we also evaluate the potential application of FEM and the problems one faces when trying to generate a large amount of data for training using FEM. Highlights: Evaluation of post-processing algorithms for infrared thermography for developing an intelligent failure analysis system. Evaluation of algorithms based on signal to background contrast and the ease of finding the image with the most information. The combination of thermal signal reconstruction (TSR) and principal component thermography (PCT) gave the best results. Exploring the role of finite element modelling in data augmentation for training a neural network. Study the influence of the element size in finite element modelling and the surface temperature images.