Patrick Rostam-Khani

Zero-failure strategies in mission critical applications are pushing reliability towards physics-of-degradation driven reliability testing, beyond the current state-of-the-art test methods. This session presents a highly accelerated vibration test approach linking in‑situ electrical signals directly to solder damage evolution. Physically non-destructive, AI-assisted 3D X-ray microscopy replaces the inaccurate, slow, and more labour-intensive physical cross-sectioning method. This shortens physical analysis cycles and enables remaining useful lifetime prediction under real-world stress loading scenarios.

Dr. Patrick Rostam Khani is currently working as Principal Engineer in the Surface/Material Analysis Group at NXP Semiconductors, based in Nijmegen (The Netherlands). With more than 25 years of experience in Material and Surface Analysis in several roles within the Product Diagnostic and Quality Center Organization of NXP, he works at the intersection of materials characterization, physics of failure, and data driven reliability modeling for advanced electronic packages. Patrick’s work contributes to linking physical degradation mechanisms to measurable signals and simulation models, enabling predictive assessment of reliability under realistic operating conditions. He is actively involved in applying AI assisted approaches to support zero failure strategies into electronics. Patrick received his Ph.D. in Instrumental Analytical Chemistry from the Johann Wolfgang Goethe University of Frankfurt am Main.

Michael Fritzsche

Failure analysis in semiconductor manufacturing requires fast and reliable access to many different data sources. As IT environments continue shifting toward cloud‑based solutions, traditional, separate workflows make it difficult to keep analysis efficient and consistent.
In this talk, I will show how combining bitmap data, inline‑defect inspection results, scan‑diagnosis output, FA results , and electrical sort information into one integrated digital workflow, which improves the turnaround time and quality of failure analysis. Automated data alignment and correlation help engineers identify patterns and potential root causes much earlier in the process.
The unified workflow has been demonstrated to reduce manual effort, enhance transparency, and ensure that all relevant information is available when required for failure analysis. This results in faster diagnostics, improved support for engineering teams, and greater customer satisfaction, because decisions can be made based on complete, up‑to‑date data.

Michael Fritzsche is a Central Labs Staff Technician with over 20 years of broad failure analysis experience.
Since 2004, he has worked as a Physical Failure Analysis Technician, initially focusing on bitmap, logic, and nanoprobing on various analytical tools. Today, he supports diverse operational areas including bitmap, scan diagnosis, Avalon configurations, and managing the Failure Analysis request system, giving him strong insight into product lifecycles and FA workflows. He also enhanced the lab’s IT infrastructure by developing web‑based visualization tools and automated processes that improve transparency, data access, and efficiency for engineering teams.

Lorenz Heinemann 

Scanning Acoustic Microscopy (SAM) is widely used in semiconductor failure analysis, but interpreting its data remains challenging. In practice, measurement noise, caused by complex packaging structures and material variations, can make very similar signals appear different, highlighting the instability of the interpretation process. This talk uses noise in SAM data as an example to illustrate how an AI-driven workflow can improve analysis. It demonstrates how structured data, automated evaluation, and a cloud-based platform can streamline handling, processing, and comparing datasets, enabling faster, more consistent, and reproducible failure analysis in practical workflows.

Lorenz Heinemann is a PhD candidate and researcher at the Fraunhofer IMWS in the Department of Non-Destructive Defect Localization. He earned his master’s degree in Computer Science from Leipzig University in 2023. His research centers on acoustic microscopy, with a focus on developing innovative AI-based analysis methods and intuitive software solutions. Through his work, he aims to enhance the accessibility and effectiveness of acoustic microscopy, particularly from a software perspective.

Leo Svenningsson

Semiconductor failure analysis increasingly benefits from machine learning, but individual organizations often lack enough data to build robust and accurate models. At the same time, building databases across companies is typically restricted due to intellectual property and confidentiality concerns. Federated Learning provides a collaborative approach where multiple partners can jointly train a machine learning model without sharing their underlying data. This talk introduces the fundamentals of Federated Learning and explains how it enables failure analysis partners to combine knowledge from distributed datasets while keeping proprietary data local.

Dr. Leo Svenningsson is researcher at RISE, Research Institutes of Sweden, working on AI-driven methods for electronics failure analysis and reliability.

Amit Choudhary

Failure analysis (FA) in microelectronics is increasingly challenging due to miniaturization and the need for precise defect localization. Traditional methods rely on manual, time-intensive inspection. We propose a digital workflow using a domain-specific multi-task model for joint detection and segmentation of defect such as voids and cracks. This enables multi-scale learning across devices and resolutions, improving generalization while reducing annotation effort and speeding transfer learning. The developed workflows when validated against expert-annotated data using metrics such as intersection over union (IoU) and F1-score shows detection accuracies of 82–93% across two use cases with varying defect types and imaging conditions. Processing speed improved by over 30x compared to conventional pipelines due to automated feature extraction and reduced model tuning. Results indicate a reduction of over 70% in total effort, primarily due to minimizing the need for extensive pixel-level labeling and eliminating the requirement for separate models for each defect type (e.g. voids). Overall, the approach streamlines FA and enables scalable, high-throughput, data-driven defect analysis.

Dr. Ing Amit Kumar Choudhary is currently working as a Head of AI Applications at Matworks GmbH and has experience in developing business-specific solutions related to automated microscopy using AI approaches and computer vision techniques. He has background in materials microscopy, image analysis, machine learning.

Bernhard Fichtl

Failure analysis increasingly depends on high‑resolution imaging, but extracting quantitative information from large datasets is still manual and slow. This talk explores how instance segmentation and other AI methods can add concrete value to failure analysis workflows. We compare classical & AI-based computer vision approaches and discuss their advantages & disadvantages. In a short live demo we will show a few examples from the EU project FA2IR and demonstrate gains in throughput, repeatability and traceability.

Dr. Bernhard Fichtl is working as a product line manager for Carl Zeiss Microscopy GmbH. He is responsible for multiple cloud- and server-based SW products. Before taking over that role, he supported various business units at ZEISS in exploring, designing and building new and innovative solutions. Bernhard has a background in physics and loves to experiment with new technologies