NAND Electrical Failure Analysis (EFA) Engineer

We are seeking a dedicated Electrical Failure Analysis Engineer who is committed to detecting problems, making impactful changes, and exploring innovative ways to predict device performance. As a company that values People, Innovation, Customer Focus, Tenacity, and Collaboration, we offer a dynamic and inclusive work environment where you can thrive and excel.

In Micron's NTI Singapore department, as an Electrical Failure Analysis Engineer, you will provide yield analysis for next-gen Micron products. Become an expert in electrical failure analysis methods for the identification of physical failures. Identify and communicate yield issues and assist with root cause analysis for process deviations. Analyze data to determine failure sources.

Responsibilities include, but not limited to:

• Analyzing failed dies from probe testing
• Conducting circuit analysis to characterize probe failures
• Performing EFA/FI using bench testers and emission microscopes
• Applying layout/schematic models to identify fail signatures
• Directing PFA to identify root cause failure analysis
• Applying schematics and layouts to identify abnormalities
• Detailing failed conditions and test results accurately

Successful candidates for this position will have:

• 2 years Experience with Micron NAND testing with uMate or Magnum tester operation
• 2 years of experience of NAND Electrical Failure Analysis & Characterization
• Experience on NAND device operations
• Strong experience in NAND testing strategies to bring out emission
• Drive failure analysis initiatives for the yield improvement team by crafting and delivering new methodologies, capabilities, and innovative ways for failure analysis
• A strong grasp of process flow and the interaction of processes and devices
• Outstanding analytical, communication, and reporting skills, with the ability to present findings effectively
• In-depth understanding of CMOS process fabrication levels, steps, and methodologies
• Proficiency in data analysis, machine learning, and statistical modeling to derive actionable insights from complex datasets is an added advantage

Education:

• BS in Materials Science