Advances in High-Resolution Non-Destructive Defect Detection and Localization Enhanced by Intelligent Signal Processing

In development and production of microelectronics products the assessment of the condition of either the full component or only specific parts is of high relevance. To allow for screening and monitoring and to leave the part unaltered the inspection techniques are required to operate non-destructively. While this allows for full-cover final inspection it also enables repetitive monitoring beneficial for the exploration of material interactions, potential subsequent defect formations and consequently for failure isolation. With increasing complexity inherent in advancing microelectronic technologies, high reliability, robustness and thus, successful failure analysis is essential. The correspondingly shrinking feature sizes and the involvement of heterogeneous materials highly challenge existing techniques. Furthermore, the interpretation of the acquired data becomes increasingly difficult requiring specifically skilled operating personal. In previous studies machine learning (ML) approaches have been developed and evaluated for their ability to analyze signals acquired by scanning acoustic microscopy (SAM) with the goal of automated defect detection, characterization, and failure isolation. The present paper investigates different ML architectures to analyze the time signals after transformation into the spectral- and wavelet domains. Results showed that 2D CNNs analyzing the acquired acoustic signals in the wavelet domain representation performed best, however at the expense of additional computational effort. Furthermore, ML-based analysis was explored for its potential to locate and isolate electrically active defects in the depth-dimension based on thermal emissions using lock-in thermography (LIT). Obtained LIT-related results are promising, however require further research to fully enfold its potential. It was further found that transfer properties of the inspection tools interfere with the defect specific signal features and thus so far tie the trained models to the specific equipment used. Future work should therefore focus on removing the specific tool related transfer characteristics of the equipment from the measurement data to allow for intra-tool compatibility and thus a more generalized application. (Abstract)