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Mario Wolf
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Proceedings Papers
ISTFA2023, ISTFA 2023: Conference Proceedings from the 49th International Symposium for Testing and Failure Analysis, 448-451, November 12–16, 2023,
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This paper investigates the enhanced inspection of High Bandwidth Memory (HBM) stacks using Scanning Acoustic Microscopy (SAM). As the multi-layer structure is quite complex, sophisticated signal processing methods are employed. To improve detection capabilities and inspection time, the Synthetic Aperture Focusing Technique (SAFT) is utilized. In contrast to previous trials applying SAFT on SAM data, this contribution introduces Near Field SAFT. Reconstruction is also performed for layers between the transducer and its focus, in the near field of the transducer. This approach allows for measurements with common working distances, providing higher frequencies and improved resolution. Systematic evaluations are conducted on various measurement setups and transducers with different center frequencies and focal lengths in order to determine the most optimal measurement setup.
Proceedings Papers
ISTFA2022, ISTFA 2022: Conference Proceedings from the 48th International Symposium for Testing and Failure Analysis, 21-27, October 30–November 3, 2022,
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Minor flaws are becoming extremely relevant as the complexity of the semiconductor package evolves. Scanning acoustic microscopy is one analytical tool for detecting flaws in such a complex package. Minor changes in the reflected signal that could indicate a fault can be lost during image reconstruction, despite the high sensitivity. Because of recent AI (Artificial Intelligence) advancements, more emphasis is being placed on developing AI-based algorithms for high precision-automated signal interpretation for failure detection. This paper presents a new deep learning model for classifying ultrasound signals based on the ResNet architecture with 1D convolution layers. The developed model was validated on two test case scenarios. One use case was the detection of voids in the die attach, the other the detection of cracks below bumps in Flip-chip samples. The model was trained to classify signals into different classes. Even with a small dataset, experiment results confirmed that the model predicts with a 98 percent accuracy. This type of signal-based model could be extremely useful in situations where obtaining large amounts of labeled image data is difficult. Through this work we propose an intelligent signal classification methodology to automate high volume failure analysis in semiconductor devices.