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.