Automatized failure analysis of tungsten coated TSVs via scanning acoustic microscopy

2016 ◽  
Vol 64 ◽  
pp. 370-374 ◽  
Author(s):  
E. Grünwald ◽  
J. Rosc ◽  
R. Hammer ◽  
P. Czurratis ◽  
M. Koch ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy

Failure Analysis of Broken Stitch Bonds in TSSOP Packages Using Scanning Acoustic Microscopy (SAM) and Time Domain Reflectometry (TDR)

2004 ◽  
Author(s):  
Bilal Abd-AlRahman ◽  
Corey Lewis ◽  
Todd Simons
Keyword(s):  
Failure Analysis ◽  
Mechanical Stress ◽  
Time Domain ◽  
Open Circuit ◽  
Time Domain Reflectometry ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
Root Cause ◽  
Analysis Application

Abstract A failure analysis application utilizing scanning acoustic microscopy (SAM) and time domain reflectometry (TDR) for failure analysis has been developed to isolate broken stitch bonds in thin shrink small outline package (TSSOP) devices. Open circuit failures have occurred in this package due to excessive bending of the leads during assembly. The tools and their specific application to this technique as well as the limitations of C-SAM, TDR and radiographic analyses are discussed. By coupling C-SAM and TDR, a failure analyst can confidently determine whether the cause of an open circuit in a TSSOP package is located at the stitch bond. The root cause of the failure was determined to be abnormal mechanical stress placed on the pins during the lead forming operation. While C-SAM and TDR had proven useful in the analysis of TSSOP packages, it can potentially be expanded to other wire-bonded packages.

Failure and Stress Analysis of Cu TSVs Using GHz-Scanning Acoustic Microscopy and Scanning Infrared Polariscopy

2015 ◽  
Author(s):  
Ingrid De Wolf ◽  
Ahmad Khaled ◽  
Martin Herms ◽  
Matthias Wagner ◽  
Tatjana Djuric ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Stress Analysis ◽  
Rayleigh Waves ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
Through Silicon Vias ◽  
Stress Anisotropy ◽  
Silicon Vias ◽  
Ic Technology ◽  
Detection Of Defects

Abstract This paper discusses the application of two different techniques for failure analysis of Cu through-silicon vias (TSVs), used in 3D stacked-IC technology. The first technique is GHz Scanning Acoustic Microscopy (GHz- SAM), which not only allows detection of defects like voids, cracks and delamination, but also the visualization of Rayleigh waves. GHz-SAM can provide information on voids, delamination and possibly stress near the TSVs. The second is a reflection-based photoelastic technique (SIREX), which is shown to be very sensitive to stress anisotropy in the Si near TSVs and as such also to any defect affecting this stress, such as delamination and large voids.

Scanning Acoustic Microscopy for Solder Joint Failure Analysis and Design Improvements

2008 ◽  
Author(s):  
Ramesh Varma ◽  
Jeffrey Bartolovitch ◽  
Victor Brzozowski ◽  
Carl Sokolowski
Keyword(s):  
Solder Joint ◽  
Failure Analysis ◽  
Thermal Cycling ◽  
Electrical Power ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
Joint Failure ◽  
Analysis And Design ◽  
Cte Mismatch ◽  
Bond Pads

Abstract This paper reports using Scanning Acoustic Microscopy for solder joint failure analysis and process and design improvements. There are reliability concerns associated with solder voids or non-wetting of the solder to the bond pads which is particularly important for higher electrical power or temperature applications. Defects in solder can also occur and grow during operation and thermal cycling. Sonoscan is an attractive non-destructive test to characterize solder joints and is often used to study the growth of defects during life test simulations. X-ray imaging cannot identify very small defects, particularly non-wetting and delamination because of poor resolution. The instrument used in this study was a CSAM (C-Mode Scanning Acoustic Microscopy) operating in reflection mode at 30-100 MHz. We have identified voids inherent in the solder layer as well as delamination at the package to solder and solder to heat-sink interfaces. C-SAM results confirmed that the delamination was caused by CTE mismatch of the materials as well as the mechanical stresses caused by higher level package integration and module assemblies. Thermal cycling studies have shown that typically the voids do not grow whereas delamination does. These results were used to improve thermal heat-sinking and product reliability by minimizing defects in solder joint by changes in process and mechanical designs.

Machine Learning Assisted Signal Analysis in Acoustic Microscopy for Non-Destructive Defect Identification

2019 ◽  
Author(s):  
Michael Kögel ◽  
Sebastian Brand ◽  
Frank Altmann
Keyword(s):  
Machine Learning ◽  
Failure Analysis ◽  
Human Error ◽  
Flip Chip ◽  
Acoustic Microscopy ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scanning Acoustic Microscopy ◽  
Chip Technology

Abstract 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.

GHz-Scanning Acoustic Microscopy Combined with ToF-SIMS/AFM for Wafer-Level Failure Analysis of Bonding Interfaces

2019 ◽  
Author(s):  
Ingrid De Wolf ◽  
Ahmad Khaled ◽  
Alexis Franquet ◽  
Valentina Spampinato ◽  
Thierry Conard ◽  
...  
Keyword(s):  
Chemical Analysis ◽  
Failure Analysis ◽  
Ion Beam ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
Wafer Level ◽  
Tof Sims

Abstract 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).

Failure Analysis Using Scanning Acoustic Microscopy for Diagnostics of Electronic Devices and 3D System Integration Technologies

2012 ◽  
Author(s):  
Peter Czurratis ◽  
Peter Hoffrogge ◽  
Sebastian Brand ◽  
Frank Altmann ◽  
Matthias Petzold
Keyword(s):  
Failure Analysis ◽  
System Integration ◽  
New Technologies ◽  
Failure Detection ◽  
Acoustic Microscopy ◽  
Scanning Acoustic Microscopy ◽  
3D Integration ◽  
Detection Techniques ◽  
Interface Defects ◽  
Custom Made

Abstract 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.

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