Full Job Description
As a Lead DRAM Device Engineer, you will provide technical leadership for device development, characterization, and yield improvement activities that support next-generation DRAM technologies. This role plays a critical part in enabling successful fab ramps, resolving complex device challenges, and driving product quality and performance. You will serve as a key technical resource across cross-functional teams while mentoring engineers and shaping technical strategy.
Responsibilities:
• Lead DRAM device activities and serve as the primary technical point of contact for device engineering initiatives
• Drive device characterization, electrical data analysis, and issue segmentation to improve yield, performance, and product quality
• Support new node introductions, technology transfers, and fab ramp activities through root-cause analysis and corrective action leadership
• Provide technical direction across Process Integration, Product Engineering, Design, Quality, and Failure Analysis teams to resolve high-impact manufacturing challenges
• Mentor engineers and promote best practices in device analysis, experimentation, and data-driven problem solving
Minimum Qualifications:
• M.S. or Ph.D. in Electrical Engineering, Materials Engineering, or a related field
• Experience supporting new node introductions, production yield ramps, and/or technology transfer activities
• Strong knowledge of semiconductor device physics, device characterization, and electrical data analysis
• Experience with statistical analysis and data-driven problem-solving methodologies
• Proven ability to lead technical discussions and collaborate effectively across cross-functional teams
Preferred Qualifications:
• Hands-on experience with memory technologies such as DRAM, NAND, or NOR
• Understanding of memory architecture, array operation, and margin bin analysis
• Experience leading technical initiatives or mentoring engineers
• Experience with AI tools and data analytics applications in semiconductor manufacturing
• Experience using AI-assisted analytics to enhance data interpretation, anomaly detection, and yield improvement initiatives.
