About Yougui Liao

In organizations facing data quality issues, the disconnect between data producers and consumers leads to problems. For instance, software engineers may not know who uses their data, data scientists may lack insight into upstream changes, and data engineers are blamed for pipeline failures. This lack of visibility creates data quality problems similar to code quality issues in software engineering.

I specialize in automation applications for improving IC yields and conducting failure analysis. Additionally, I contribute to machine learning developments aimed at enhancing yield in semiconductor processes.

As an experienced senior process integration and yield enhancement engineer with a PhD degree, I have worked aggressively on process integration in the most challenging key processes, such as the active area in an array, RDL, contacts in FEOL and MOL modules, and so on. My focus is to intersect project timelines and yield goals, aggressively develop and innovate new techniques to monitor and optimize process window (PW) enhancement from thin films and dry etch processes. I have successfully developed efficient techniques and methodologies to compare the processes of key modules across fabs in the US and Asia. Additionally, I have conducted characterizations of various IC devices and materials using electron microscopes and related techniques to understand the problems in 'native' materials and devices at various companies and universities for more than 20 years. I possess a fundamental understanding of the application advantages and limitations of integrated circuits, Python and C++ programming, microanalysis, and instrumentation.

LinkedIn: https://www.linkedin.com/in/yougui-liao-10173651/

Online books: www.globalsino.com

Main interests: Analytics and Technology Automation (ATA) in semiconductor industry

Some publications on automations:

Example of public services: ISTFA Session Chair of the AI Applications for Failure Analysis