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.

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.

New semiconductor chip technologies and technologies for 3D integration require information’s of packaging and interface defects in 3 dimensions, that means the lateral dimension of the defect and the location inside the device or package must be defined. In this paper, new methodical approaches for non destructive failure analysis on 3D integrated TSV samples are introduced. The concepts combine improved scanning acoustic microscopy (SAM) imaging hardware with unique software solutions for defect identification and quantitative analysis of mechanical properties using scanning acoustic investigations. In case of MEMS 3D integration, e.g. based on direct bonding, related interface defects must be investigated by SAM. With respect to 3D integration applications, the potential of recent SAM improvements applying specifically adapted hardware and custom-made signal processing algorithms will be discussed. Examples of SAM-based failure detection techniques for the application in 3D integration are demonstrated. New technologies are shown to improve the through put of fully wafer scanning using scanning acoustic microscopy. To improve the defect resolution, a new transducer design was developed to increase defect resolution and signal to noise for interface characterisation.

In industrial manufacturing of microelectronic components, non-destructive failure analysis methods are required for either quality control or for providing a rapid fault isolation and defect localization prior to detailed investigations requiring target preparation. Scanning acoustic microscopy (SAM) is a powerful tool enabling the inspection of internal structures in optically opaque materials non-destructively. In addition, depth specific information can be employed for two- and three-dimensional internal imaging without the need of time consuming tomographic scan procedures. The resolution achievable by acoustic microscopy is depending on parameters of both the test equipment and the sample under investigation. However, if applying acoustic microscopy for pure intensity imaging most of its potential remains unused. The aim of the current work was the development of a comprehensive analysis toolbox for extending the application of SAM by employing its full potential. Thus, typical case examples representing different fields of application were considered ranging from high density interconnect flip-chip devices over wafer-bonded components to solder tape connectors of a photovoltaic (PV) solar panel. The progress achieved during this work can be split into three categories: Signal Analysis and Parametric Imaging (SA-PI), Signal Analysis and Defect Evaluation (SA-DE) and Image Processing and Resolution Enhancement (IP-RE). Data acquisition was performed using a commercially available scanning acoustic microscope equipped with several ultrasonic transducers covering the frequency range from 15 MHz to 175 MHz. The acoustic data recorded were subjected to sophisticated algorithms operating in time-, frequency- and spatial domain for performing signal- and image analysis. In all three of the presented applications acoustic microscopy combined with signal- and image processing algorithms proved to be a powerful tool for non-destructive inspection.