| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Van Driel, Willem
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2024Training Convolutional Neural Networks with Confocal Scanning Acoustic Microscopy Imaging for Power QFN Package Delamination Classification
- 2023Heterogeneous Integration of Diamond Heat Spreaders for Power Electronics Applicationcitations
- 2022Interphase effect on the effective moisture diffusion in epoxy–SiO2 compositescitations
- 2021Facile synthesis of ag nanowire/tio2 and ag nanowire/tio2/go nanocomposites for photocatalytic degradation of rhodamine bcitations
- 2021Exploring water and ion transport process at silicone/copper interfaces using in-situ electrochemical and Kelvin probe approachescitations
- 2018Solid State Lighting Reliability Part 2
- 2016Creep fatigue models of solder jointscitations
- 2015An overview of scanning acoustic microscope, a reliable method for non-destructive failure analysis of microelectronic componentscitations
- 2010Designing for reliability using a new Wafer Level Package structure
- 2009Virtual Prototyping for PPM-level Failures in Microelectronic Packages
- 2009Reliability of Wafer Level Thin Film MEMS Packages during Wafer Backgrinding
- 2008Effect of aging of packaging materials on die surface cracking of a SiP carrier
- 2008Die Fracture Probability Prediction and Design Guidelines for Laminate-Based Over-Molded Packages
- 2007Modeling of the mechanical stiffness of the GaP/GaAs nanowires with point defects/stacking faults
- 2007Efficient damage sensitivity analysis of advanced Cu/low-k bond pad structures by means of the area release energy criterion
- 2007Correlation between chemistry of polymer building blocks and microelectronics reliability
- 2007Measuring the through-plane elastic modulus of thin polymer films in situ
- 2007Characterization of moisture properties of polymers for IC packaging
- 2006Mixed Mode Bending Test for Interfacial Adhesion in Semiconductor Applications
- 2005The precision of large radio continuum source catalogues. An application of the SPECFIND toolcitations
Places of action
| Organizations | Location | People |
|---|
document
Training Convolutional Neural Networks with Confocal Scanning Acoustic Microscopy Imaging for Power QFN Package Delamination Classification
Abstract
This study introduces a training protocol utilizing Convolutional Neural Networks (CNNs) and Confocal Scanning Acoustic Microscopy (CSAM) imaging techniques to classify Power Quad Flat No-leads (PQFN) package delamination. The investigation involves empty PQFN packages with varied substrate metallizations subjected to thermal cycling. Four delamination classes were labeled: Die-pad delamination (Class-A), Bond-pad delamination (Class-B), both Die-pad and Bond-pad delamination (Class-C), and No delamination (Class-D). Due to data imbalance, additional randomness was introduced for distribution balancing. Residual Networks (ResNet-18) based CNN model was selected for classification. Five-fold cross-validation assessed overfitting performance concerning input data size, image resolution, and batch size. The ResNet-18 prediction performance was evaluated using precision and recall metrics, with the model achieving average precision and recall scores of 0.86/1 and 0.83/1, respectively. Additionally, a comparison of delamination among different substrate metallizations was presented with Ag and NiPdAu indicating significant delamination compared to bare Cu substrate. This study pioneers the integration of CNNs with CSAM imaging for package defect detection and classification, laying the groundwork for future research to address the complex interplay of multiple failure mechanisms in functional packages.