Title: Machine Learning-Based DefectClassification and Anomaly Detection in Advanced IC Package 3D InterconnectsUsing Scanning Acoustic Microscopy
Technical Description:
As transistor scaling approaches its physical limits,advanced packaging has become essential to drive semiconductor performance,system integration, and reliability beyond traditional Moore's Law. In suchpackaging architectures, manufacturing defects such as micro-cracks, badwetting, and underfill voids can jeopardize device reliability. ScanningAcoustic Microscopy (SAM) can non-destructively detect these defects, butcurrent inspection relies heavily on slow, subjective manual review. Automatingthis process with machine learning would greatly speed up industrial failureanalysis, yet two main bottlenecks remain: defects occur too rarely to buildlarge training datasets, and tiny micro-cracks look almost identical to healthyjoints in acoustic scans.
Preliminary work has demonstrated that depth-resolvedC-SAM gating combined with anomaly detection substantially improves defectscreening over conventional supervised models. However, 2D C-SAM images discardvital phase, frequency, and time-of-flight information embedded in the rawacoustic waveform.
Cross-sectional view of a solder bump in a FCBGA (flip-chip ball grid array) package. Extracted from [1]
C-SAM images at various depths ranging from solder bump to contact pad. Extracted from [1].
Objectives:
The primary goal of this project is to build anautomated, highly accurate failure analysis pipeline for advancedmicro-interconnects by overcoming the severe data scarcity inherent topackaging failure analysis. This project will investigate novel machinelearning frameworks capable of achieving high detection accuracy despiteextreme class imbalance and limited defective labels. The proposed methodology shouldaim to maximize model generalization and accuracy across varied interconnectarchitectures. In summary, some potential key objectives include:
References:
[1] M. Gomes Abrantes, "Defect Detection inAdvanced IC Interconnects," Master's Thesis, Faculty of EngineeringSciences, KU Leuven & imec, 2026.
[2] J. Snell, K. Swersky, and R. Zemel. Prototypicalnetworks for few-shot learning. In Advances in Neural Information ProcessingSystems (NeurIPS), 2017.
[3] S. Brand, P. Czurratis, P. Hoffrogge, and M.Petzold. Automated inspection and classification of flip-chip-contacts usingscanning acoustic microscopy. Microelectronics Reliability, 50(9-11):1496-1501,2010.
[4] C. S. Lee, G.-M. Zhang, D. M. Harvey, and A. Qi.Characterization of microcrack propagation through analysis of edge effect inacoustic microimaging of microelectronic packages. NDT & E International,79:19-27, 2016.
[5] M. Wolf, A. Sukumaran Nair, P. Hoffrogge, E.Kühnicke, and P. Czurratis. Improved failure analysis in scanning acousticmicroscopy via advanced signal processing techniques. Microelectronics andReliability, 138:114618, 2022.
Desired Qualifications:
Contacts: < email deleted for security reasons >
Type of internship: Master internship, PhD internship
Duration: 6 months to 1 year
Required educational background: Electrotechnics/Electrical Engineering, Materials Engineering, Physics, Nanoscience & Nanotechnology, Mechanical Engineering, Electromechanical engineering, Other
University promotor: Houman Zahedmanesh (KU Leuven)
Supervising scientist(s): For further information or for application, please contact Jeffrey Gutierrez (< email deleted for security reasons >)
The reference code for this position is 2026-INT-330. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
Incomplete applications will not be considered.
Technical Description:
As transistor scaling approaches its physical limits,advanced packaging has become essential to drive semiconductor performance,system integration, and reliability beyond traditional Moore's Law. In suchpackaging architectures, manufacturing defects such as micro-cracks, badwetting, and underfill voids can jeopardize device reliability. ScanningAcoustic Microscopy (SAM) can non-destructively detect these defects, butcurrent inspection relies heavily on slow, subjective manual review. Automatingthis process with machine learning would greatly speed up industrial failureanalysis, yet two main bottlenecks remain: defects occur too rarely to buildlarge training datasets, and tiny micro-cracks look almost identical to healthyjoints in acoustic scans.
Preliminary work has demonstrated that depth-resolvedC-SAM gating combined with anomaly detection substantially improves defectscreening over conventional supervised models. However, 2D C-SAM images discardvital phase, frequency, and time-of-flight information embedded in the rawacoustic waveform.
Cross-sectional view of a solder bump in a FCBGA (flip-chip ball grid array) package. Extracted from [1]
C-SAM images at various depths ranging from solder bump to contact pad. Extracted from [1].
Objectives:
The primary goal of this project is to build anautomated, highly accurate failure analysis pipeline for advancedmicro-interconnects by overcoming the severe data scarcity inherent topackaging failure analysis. This project will investigate novel machinelearning frameworks capable of achieving high detection accuracy despiteextreme class imbalance and limited defective labels. The proposed methodology shouldaim to maximize model generalization and accuracy across varied interconnectarchitectures. In summary, some potential key objectives include:
- Expand inspection beyond C-SAM images by processing rawA-scan waveforms, extracting phase shifts, time-of-flight variations, and defectsignatures.
- Benchmark modern data-efficient architectures (e.g., few-shot prototypical networks, metric learning, etc.) to maximize sensitivity toward rare defect classes.
- Leverage forward acoustic simulation tools to synthesize realistic, defect-injected waveforms and scans, directly mitigating class imbalance and preventing model overfitting.
- Test model robustness across diverse package types (e.g., FCBGA with exposed die, over molded, etc.), validating predictions against destructive cross-sectional inspections.
References:
[1] M. Gomes Abrantes, "Defect Detection inAdvanced IC Interconnects," Master's Thesis, Faculty of EngineeringSciences, KU Leuven & imec, 2026.
[2] J. Snell, K. Swersky, and R. Zemel. Prototypicalnetworks for few-shot learning. In Advances in Neural Information ProcessingSystems (NeurIPS), 2017.
[3] S. Brand, P. Czurratis, P. Hoffrogge, and M.Petzold. Automated inspection and classification of flip-chip-contacts usingscanning acoustic microscopy. Microelectronics Reliability, 50(9-11):1496-1501,2010.
[4] C. S. Lee, G.-M. Zhang, D. M. Harvey, and A. Qi.Characterization of microcrack propagation through analysis of edge effect inacoustic microimaging of microelectronic packages. NDT & E International,79:19-27, 2016.
[5] M. Wolf, A. Sukumaran Nair, P. Hoffrogge, E.Kühnicke, and P. Czurratis. Improved failure analysis in scanning acousticmicroscopy via advanced signal processing techniques. Microelectronics andReliability, 138:114618, 2022.
Desired Qualifications:
- Strong programming skills in Python (or MATLAB) and machine learning frameworks (e.g., PyTorch, scikit-learn).
- Knowledge of signal processing, computer vision, or acoustic wave propagation.
- Interest in semiconductor packaging, failure analysis, and hands-on lab metrology (SAM).
Contacts: < email deleted for security reasons >
Type of internship: Master internship, PhD internship
Duration: 6 months to 1 year
Required educational background: Electrotechnics/Electrical Engineering, Materials Engineering, Physics, Nanoscience & Nanotechnology, Mechanical Engineering, Electromechanical engineering, Other
University promotor: Houman Zahedmanesh (KU Leuven)
Supervising scientist(s): For further information or for application, please contact Jeffrey Gutierrez (< email deleted for security reasons >)
The reference code for this position is 2026-INT-330. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.
