Presentation slides for the ISTFA 2023 Tutorial session “Fundamental Considerations in the Justification, Design, and Construction of an Analytical Laboratory for High Tech Imaging and Processing Tools.”

Presentation slides for the ISTFA 2023 Tutorial session “Power Semiconductor Failure Analysis Tutorial.”

Presentation slides for the ISTFA 2023 Tutorial session “Metrologies to Monitor Contamination Fallout in the Cleanroom.”

Presentation slides for the ISTFA 2023 Tutorial session “Reliability and Failure Analysis of SiC Power Devices and Modules.”

Presentation slides for the ISTFA 2023 Tutorial session “Machine Learning Based Data and Signal Analysis Methods for the Application in Failure Analysis (2023 Update).”

This presentation is an introduction to machine learning techniques and their application in semiconductor failure analysis. The presentation compares and contrasts supervised, unsupervised, and reinforcement learning methods, particularly for neural networks, and lays out the steps of a typical machine learning workflow, including the assessment of data quality. It also presents case studies in which machine learning is used to detect and classify circuit board defects and analyze scanning acoustic microscopy (SAM) data for blind source separation.

This presentation provides a systematic approach for assessing contamination risks in semiconductor manufacturing environments and determining an appropriate course of action. It maps out a typical wafer production line and identifies potential entry points for specific contaminants. It explains how to perform a contamination risk assessment and prioritize action items and how to select an analytical technique based on where, in the stack, the contaminant resides. It highlights the differences between direct and indirect techniques and shows where they are likely to be used. It also presents a detailed analytical decision flow chart and three concise case studies.