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Lock-in thermography (LIT) has been successfully applied in different excitation and analysis modes including classical LIT, analysis of the time-resolved temperature response (TRTR) upon square wave excitation and TRTR analysis in combination with arbitrary waveform stimulation. The results obtained by both classical square wave- and arbitrary waveform stimulation showed excellent agreement. Phase and amplitudes values extracted by classical LIT analysis and by Fourier analysis of the time resolved temperature response also coincided, as expected from the underlying system theory. In addition to a conceptual test vehicle represented by a point-shaped thermal source, two semiconductor packages with actual defects were studied and the obtained results are presented herein. The benefit of multi-parametric imaging for identification of a defect’s lateral position in the presence of multiple hot spots was also demonstrated. For axial localization, the phase shift values have been extracted as a function of frequency [4]. For comparative validation, LIT analyses were conducted in both square wave and arbitrary waveform excitation using custom designed and sample-specific stimulation signals. In both cases result verification was performed employing X-ray, scanning electron microscopy (SEM) and energy dispersive x-ray (EDX) as complementary techniques.

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Signal processing and data interpretation in scanning acoustic microscopy is often challenging and based on the subjective decisions of the operator, making the defect classification results prone to human error. The aim of this work was to combine unsupervised and supervised machine learning techniques for feature extraction and image segmentation that allows automated classification and predictive failure analysis on scanning acoustic microscopy (SAM) data. In the first part, conspicuous signal components of the time-domain echo signals and their weighting matrices are extracted using independent component analysis. The applicability was shown by the assisted separation of signal patterns to intact and defective bumps from a dataset of a CPU-device manufactured in flip-chip technology. The high success-rate was verified by physical cross-sectioning and high-resolution imaging. In the second part, the before mentioned signal separation was employed to generate a labeled dataset for training and finetuning of a classification model based on a one-dimensional convolutional neural network. The learning model was sensitive to critical features of the given task without human intervention for classification between intact bumps, defective bumps and background. This approach was evaluated on two individual test samples that contained multiple defects in the solder bumps and has been verified by physical inspection. The verification of the classification model reached an accuracy of more than 97% and was successfully applied to an unknown sample which demonstrates the high potential of machine learning concepts for further developments in assisted failure analysis.