New College Grad - ID1 ATE Process Engineer WET

Micron•Boise, ID

3d•Onsite

About The Position

Micron Technology is hiring a New College Grad for an ID1 ATE Process Engineer role in Wet Etch (WET) at its Boise, Idaho expansion. The posting describes work on starting up, developing, and optimizing processes to improve product quality, yield, cost, productivity, and risk management, while resolving manufacturing-line problems. The engineer will work with multifunctional teams to address process issues and improve efficiency and yield, using digital and AI-driven tools for process monitoring, data analysis, defect analysis, yield optimization, trend detection, reporting, and process control. The role focuses on semiconductor manufacturing and DRAM development. Micron describes Wet Etch as including vapor etch, dry strip, dry bevel etch, plating, bath processes, and spin processes. The posting also states that international travel may be required for onboarding and training.

Requirements

At least 2 years of industry experience working as a Process Owner within Wet Etch Process.
A bachelor's or master's degree in an engineering field, equivalent military experience, or an associate of applied science degree with at least 5 years of industry experience.
Willingness to travel internationally for onboarding and training.
Strong process-area knowledge.
DRAM development and manufacturing experience.

Nice To Haves

Strong understanding of semiconductor fabrication processes and equipment.
Excellent analytical and problem-solving skills, with a meticulous approach.
Strong communication skills and ability to collaborate in a team environment.
Proficiency in data science and programming.

Responsibilities

Develop and optimize processes to improve product quality, yield, cost, productivity, and risk management.
Resolve manufacturing-line problems and collaborate with equipment and integration engineers to identify and address production issues.
Use project management skills to complete milestones that close yield, defectivity, production, and cost gaps.
Drive improvements across owned statistical process control (SPC) charts.
Use digital, AI, LLM, and automation tools for defect analysis, yield optimization, trend detection, reporting efficiency, process monitoring, and data-driven process-control decisions.