Semiconductor Inspection Applications Engineer

Micron Technology, Inc; Boise (ID); On-site; USD 90,000 - 120,000; Full time

Job summary

Micron Technology, Inc. in Boise, Idaho is seeking a Semiconductor Equipment Applications Engineer who will own inspection equipment strategies, develop and maintain recipes for Defects of Interest, and work hands-on with optical inspection tools while partnering with engineers and vendors.

You will coordinate with RAM Equipment Sustaining Team, track tool maintenance and data trends, and apply AI techniques to improve yield and process learning in a high-volume fab environment.

Qualifications

• Bachelor’s degree or equivalent experience in Engineering or Physics.
• Knowledge of semiconductor equipment and fabrication processes.
• Experience analyzing data to support data-driven decisions.
• Strong written and verbal communication skills.
• High level of organization, attention to detail, and proactive problem solving.

Responsibilities

• Develop and maintain inspection recipes to detect issues and improve efficiency.
• Partner with module owners to ensure recipe quality and resolve issues.
• Lead projects to improve inspection efficiency, sampling strategies, and new techniques.
• Coordinate vendor applications engineering work.
• Serve as a technical liaison between Micron engineering teams and equipment suppliers.
• Collaborate with RAM Equipment Sustaining Team to track tool maintenance, SPC charts, and monitor vendor support.

Skills

Data analysis

Written communication

Verbal communication

Organizational skills

Problem solving

Education

Bachelor’s degree in Engineering or Physics

Job description

Micron Technology, Inc. in Boise, Idaho is seeking a Semiconductor Equipment Applications Engineer who will own inspection equipment strategies, develop and maintain recipes for Defects of Interest, and work hands-on with optical inspection tools while partnering with engineers and vendors.

You will coordinate with RAM Equipment Sustaining Team, track tool maintenance and data trends, and apply AI techniques to improve yield and process learning in a high-volume fab environment.