This presentation provides an overview of the terminology and concepts associated with semiconductor yield analysis, modeling, and improvement techniques. It compares and contrasts yield models and describes the steps and equipment involved in setting up yield engineering programs targeting specific failures and defects. It also includes case histories showing how different yield analysis models have been used to identify the root cause of random and systematic failures.

This presentation provides an overview of nanoprobe systems and what they reveal about defects and abnormalities in semiconductor device structures and materials. The presentation covers the basic operating principles, implementation, and capabilities of atomic force probe and beam-based imaging techniques, including AFP pico-current contrast and scanning capacitance imaging, SEM/FIB active voltage contrast imaging, and SEM/FIB electron-beam absorbed current (EBAC), induced current (EBIC), and induced resistance change (EBIRCH) imaging. It also includes guidelines for probing transistors and copper metallization and case studies in which nanoprobing was used to analyze gate oxide and substrate defects, intermittent bit cell failures, threshold voltage shifts, and time-domain popcorn noise.

This presentation provides an overview of nanoprobe systems and what they reveal about defects and abnormalities in semiconductor device structures and materials. The presentation covers the basic operating principles, implementation, and capabilities of atomic force probe and beam-based imaging techniques, including AFP pico-current contrast and scanning capacitance imaging, SEM/FIB active voltage contrast imaging, and SEM/FIB electron-beam absorbed current (EBAC), induced current (EBIC), and induced resistance change (EBIRCH) imaging. It also includes guidelines for probing transistors and copper metallization and case studies in which nanoprobing was used to analyze gate oxide and substrate defects, intermittent bit cell failures, threshold voltage shifts, and time-domain popcorn noise.

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 an overview of the terminology and concepts associated with semiconductor yield analysis, modeling, and improvement techniques. It compares and contrasts yield models and describes the steps and equipment involved in setting up yield engineering programs targeting specific failures and defects. It also includes case histories showing how different yield analysis models have been used to identify the root cause of random and systematic failures.