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.

Presentation slides for the ISTFA 2023 Tutorial session “Non-Destructive Defect Localization by Scanning Acoustic Microscopy.”

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.

This presentation covers the challenges associated with IC package inspection and shows how two nondestructive techniques, scanning acoustic microscopy and X-ray imaging, are being used to locate and identify a wide range of defects, particularly those in 3D packages and multilayer boards. It reviews the basic principles of scanning acoustic microscopy (SAM), X-ray imaging, and 3D X-ray tomography and the factors that affect image resolution and depth. It demonstrates the current capabilities of each method along with different approaches for improving resolution, contrast, and measurement time.

This presentation is an introduction to machine learning techniques and their application in semiconductor failure analysis. The presentation compares and contrasts supervised, unsupervised, and reinforcement learning methods, particularly for neural networks, and lays out the steps of a typical machine learning workflow, including the assessment of data quality. It also presents case studies in which machine learning is used to detect and classify circuit board defects and analyze scanning acoustic microscopy (SAM) data for blind source separation.

This paper discusses the basic physics of scanning acoustic microscopy, the counterfeit features it can detect, and how it compares with other screening methods. Unlike traditional optical inspection and IR and X-ray techniques, SAM can identify recycled and remarked chips by exposing ghost markings, fill material differences, delaminations from excessive handling, and popcorn fractures caused by trapped moisture. The paper presents several examples along with detailed images of these telltale signs of semiconductor counterfeiting. It also discusses the potential of developing an automated solution for detecting counterfeits on a large scale.

This paper assesses the capabilities of scanning acoustic tomography (SAT) for the analysis of bonded silicon wafers. In order to quantitatively evaluate detectability and resolution, the authors acquired images from samples prepared with artificial voids. The samples consisted of two wafers, a cap wafer and a base wafer with dry-etched pits on a silicon-oxide layer. Cap wafers of different thicknesses were used along with transducers of appropriate focal length. The paper describes the experimental setup and test procedures in detail as well as the results.

This presentation is an introduction to machine learning techniques and their application in semiconductor failure analysis. The presentation compares and contrasts supervised, unsupervised, and reinforcement learning methods, particularly for neural networks, and lays out the steps of a typical machine learning workflow, including the assessment of data quality. It also presents case studies in which machine learning is used to detect and classify circuit board defects and analyze scanning acoustic microscopy (SAM) data for blind source separation.

This presentation provides an overview of the tools and techniques that can be used to analyze failures in semiconductor devices made with 3D technology. It assesses the current state of 3D technology and identifies common problems, reliability issues, and likely modes of failure. It compares and contrasts all relevant measurement techniques, including X-ray computed tomography, scanning acoustic microscopy (SAM), laser ultrasonics, ultrasonic beam induced resistance change (SOBIRCH), magnetic current imaging, magnetic field imaging, and magneto-optical frequency mapping (MOFM) as well as time domain reflectometry (TDR), electro-optical terahertz pulsed reflectometry (EOTPR), lock-in thermography (LIT), confocal scanning IR laser microscopy, infrared polariscopy, and photon emission microscopy (PEM). It also covers light-induced voltage alteration (LIVA), light-induced capacitance alteration (LICA), lock-in thermal laser stimulation (LI-TLS), and beam-based techniques, including voltage contrast (VC), electron-beam absorbed current (EBAC), FIB/SEM 3D imaging, and scanning TEM imaging (STEM). It covers the basic principles as well as advantages and limitations of each method.

Currently gaps in non-destructive 2D and 3D imaging in PFA for advanced packages and MEMS exist due to lack of resolution to resolve sub-micron defects and the lack of contrast to image defects within the low Z materials. These low Z defects in advanced packages include sidewall delamination between Si die and underfill, bulk cracks in the underfill, in organic substrates, Redistribution Layer, RDL; Si die cracks; voids within the underfill and in the epoxy. Similarly, failure modes in MEMS are often within low Z materials, such as Si and polymers. Many of these are a result of mechanical shock resulting in cracks in structures, packaging fractures, die adhesion issues or particles movements into critical locations. Most of these categories of defects cannot be detected non-destructively by existing techniques such as C-SAM or microCT (micro x-ray computed tomography) and XRM (X-ray microscope). We describe a novel lab-based X-ray Phase contrast and Dark-field/Scattering Contrast system with the potential to resolve these types of defects. This novel X-ray microscopy has spatial resolution of 0.5 um in absorption contrast and with the added capability of Talbot interferometry to resolve failure issues which are related to defects within organic and low Z components.

Failure Analysis labs involved in customer returns always face a greater challenge, demand from customer for a faster turnaround time to identify the root cause of the failure. Unfortunately, root cause identification in failure analysis is often performed incompletely or rushing into destructive techniques, leading to poor understanding of the failure mechanism and root-cause, customer dissatisfaction. Scanning Acoustic Tomography (SAT), also called Scanning Acoustic Microscope (SAM) has been adopted by several Failure Analysis labs because it provides reliable non-destructive imaging of package cracks and delamination. The SAM is a vital tool in the effort to analyze molded packages. This paper provides a review of non-destructive testing method used to evaluate Integrated Circuit (IC) package. The case studies discussed in this paper identifies different types of defects and the capabilities of B-Scan (cross-sectional tomography) method employed for defect detection beyond delamination.

GHz-SAM using toneburst transducers is a method currently lacking the possibility to measure warped samples easily because large parts of such samples are out of focus due to the limited depth of focus of these types of transducers. This paper shows an approach to use the already established HiSA (High Speed Axis) method to overcome this disadvantage. Therefore warpage of the sample is measured by a white-light interferometer. The detected bow is parametrized and submitted in the control electronics of the HiSA. With this data the HiSA is enabled to keep the distance between sample and transducer precisely constant. This allows eliminating the influence of the warpage on the performance of this method and therefore highly increases the image quality.

This paper discusses the implementation of GHz-Scanning Acoustic Microscopy (GHz-SAM) into a wafer level scanning tool and its application for the detection of delamination at the interface of hybrid bonded wafers. It is demonstrated that the in-plane resolution of the GHz-SAM technique can be enhanced by thinning the sample. In the current study this thinning step has been performed by the ion beam of a ToF-SIMS tool containing an in-situ AFM, which allows not only chemical analysis of the interface but also a well-controlled local thinning (size, depth and roughness).

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.

Scanning Acoustic Microscopy (SAM) is a very important tool in the evaluation of molded plastic electronic components. SAM is used to non-destructively determine the configuration and quality of components using ultrasonic sound waves and consequently is an important test step in the screening, Destructive Physical Analysis (DPA) or Failure Analysis (FA) of plastic components. SAM is performed in a water bath so if internal defects are open to the surface of the device they can fill with water and become invisible to SAM.

This paper discusses the Failure Analysis methodology used to characterize 3D bonded wafers during the different stages of optimization of the bonding process. A combination of different state-of-the-art techniques were employed to characterize the 3D patterned and unpatterned bonded wafers. These include Confocal Scanning Acoustic Microscopy (CSAM) to determine the existence of voids, Atomic Force Microscopy (AFM) to determine the roughness of the films on the wafers, and the Double Cantilever Beam Test to determine the interfacial strength. Focused Ion Beam (FIB) was used to determine the alignment offset in the patterns. The interface was characterized by Auger Spectroscopy and the precession electron nanobeam diffraction analysis to understand the Cu grain boundary formation.

This paper demonstrates the application of GHz-SAM for the detection of local non-bonded regions between micron-sized Cu-pads in a wafer-to-wafer hybrid bonded sample. GHz-SAM is currently the only available non-destructive failure analysis technique that can offer this information on wafer level scale, with such high resolution.

This paper is focused on the de-soldering process on VSON package which was mounted on FR4 substrate board after being subjected to environmental stress. Abnormalities were found at package level during Scanning Acoustic Microscopy (SAM) inspection which is considered to be one of the non-destructive failure analysis processes. Root cause finding involved the investigation of the de-soldering equipment which is suspected to be one of the culprits to contribute to the defect during de-soldering process.

GHz scanning acoustic microscopy (GHz-SAM) was successfully applied for non-destructive evaluation of the integrity of back end of line (BEOL) stacks located underneath wire-bond pads. The current study investigated two sample types of different IC processes. Realistic bonding defects were artificially induced into samples and the sensitivity of the acoustic GHz-microscope towards defects in BEOL systems was studied. Due to the low penetration depth in the acoustic GHz regime, a specific sample preparation was conducted in order to provide access to the region of interest. However, the preparation stopped several microns above the interfaces of interest, thus avoiding preparation artifacts in the critical region. Cratering related cracks in the bond pads have been imaged clearly by GHz-SAM. The morphology of the visualized defects corresponded well with the results obtained by a chemical cratering test. Moreover, delamination defects at the interface between ball and pad metallization were detected and successfully identified. The current paper demonstrates non-destructive inspection for bond-pad cratering and ball-bond delamination using highly focused acoustic waves in the GHz-band and thus illustrates the analysis of micron-sized defects in BEOL layer structures that are related to wire bonding or test needle imprints.

Through Silicon Via (TSV) is the most promising technology for vertical interconnection in novel three-dimensional chip architectures. Reliability and quality assessment necessary for process development and manufacturing require appropriate non-destructive testing techniques to detect cracks and delamination defects with sufficient penetration and imaging capabilities. The current paper presents the application of two acoustically based methods operating in the GHz-frequency band for the assessment of the integrity of TSV structures.