Micron
FE OCT CPEE Dry Etch DMTS
Boise, ID - North Office · Full-time · Posted Oct 27, 2026
- Experience
- PhD with 15+ years, or a Master's degree combined with equivalent experience totaling 17+ years.
- Education
- PhD or Master's degree in Electrical Engineering, Materials Science, Chemical Engineering, Physics, or a related technical field.
About the job
Provides global technical leadership for dry etch processes, setting strategy and scaling improvements across Micron’s DRAM and NAND manufacturing network.
What you'll do
- Define dry etch technical strategy and lead improvements in yield, defectivity, process capability, productivity, cycle time, and cost.
- Develop, qualify, and scale process and equipment solutions for high-volume manufacturing, partnering with internal teams and suppliers.
- Lead global technical programs, establish governance and knowledge-sharing practices, mentor technical leaders, and advance analytics, automation, and AI-enabled solutions.
What they ask for
- Deep expertise in dry etch processes, plasma physics and chemistry, profile control, selectivity, critical dimension control, and process integration.
- Proven success developing, transferring, and optimizing plasma etching processes in semiconductor research, development, and high-volume manufacturing.
- Ability to influence technical direction, lead complex multifunctional initiatives, and communicate with senior technical and business leaders.
- Strong foundation in statistical process control, data-driven problem solving, process qualification, defectivity reduction, and manufacturing performance improvement.
Nice to have
- Broad experience in advanced dry etch technologies for DRAM, NAND, or advanced semiconductor manufacturing.
- Experience with supplier roadmaps, advanced analytics, machine learning or AI, EUV-enabled integration, or advanced manufacturing scaling.
Tools
dry etch, plasma etch, DRAM, NAND, statistical process control, machine learning, AI, EUV
AI focus
Advance AI-enabled solutions and apply analytics, machine learning, and automation to improve manufacturing learning, decision-making, and performance.
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