PhD Defense: "Knowledge Discovery in Test: Methods, Models, and Applications"

Dragoljub (Gagi) Drmanac

May 9th (Monday), 12:00pm
Harold Frank Hall (HFH), Rm 4164

As integrated circuit technology continues to scale, new and complex failure mechanisms occur forcing test to evolve from its traditional role of pass/fail screening to adaptive data-driven methods capable of diagnosing and predicting failures, modeling highly nonlinear processes, and coping with variability. Detecting and understanding failures associated with problems such as atypical test results, systematic shifts, lithographic variability, test escapes, and high volumes of noisy parametric measurements requires a holistic perspective for informed decision making. A new test paradigm must leverage both design and test data to uncover valuable knowledge that can explain and correct complex failure mechanisms while offering better tradeoffs between test cost and quality. Such an approach represents a shift from simply uncovering statistical significance in test data to a method that explains statistical findings through interpretable and actionable knowledge. This dissertation introduces a knowledge discovery process for test that eases the transition from well-established pass/fail methods to newly emerging statistical testing and modeling. A knowledge discovery approach is used to develop several automatic process flows for analyzing design and test data, enabling the extraction of both descriptive and predictive knowledge, beginning with raw data and ending with valuable insight. A variety of preprocessing, data mining, and visualization methods are used to build a unified knowledge discovery framework with several applications in test. Silicon measurements and design data from four industry products currently in production are analyzed to show how a knowledge discovery process can perform data driven failure diagnosis, outlier based failure screening, predict variability in nanoscale lithography, automate non-parametric behavioral modeling, and reduce test cost by parametric test set optimization.

About Dragoljub (Gagi) Drmanac:

Dragoljub (Gagi) Drmanac is a PhD candidate in electrical and computer engineering at the University of California Santa Barbara. His research focuses on applying various machine learning and data mining techniques to test and modeling problems. He focuses on developing appropriate data representations and corresponding similarity metrics to map difficult test problems onto machine learning frameworks, allowing quick and effective analysis.

Hosted by: Professor Li-C. Wang, Committee Chair

Snacks will be provided. Feel free to bring your own lunch.