Overview
Micron Technology’s Singapore TD (EDE-SG) department is seeking an intern to support R&D activities focused on deploying data science solutions for First-of-A-Kind (FOAK) workstation monitoring of equipment performance metrics in NAND manufacturing. The internship provides exposure to advanced semiconductor manufacturing processes, data analytics, and cross-functional collaboration with equipment, process, and manufacturing teams.
Key Responsibilities
- Design and optimize automated data collection and robotic process automation workflows.
- Develop dashboards and visualization tools to monitor equipment performance metrics.
- Collaborate with equipment development, process development, process integration, manufacturing teams, and vendors to implement robust monitoring solutions.
- Support the deployment and analysis of FOAK workstations for evaluating equipment performance.
- Document workflows, insights, and R&D outcomes to contribute to production-readiness.
Qualifications / Ideal Candidate
- Currently pursuing an undergraduate degree in a technical or analytical field (Engineering, Computer Science, Data Science, or related).
- Aptitude for research and development environments.
- Familiarity with Python/Jupyter, Tableau, Excel, JMP, and UI Path.
- Strong organizational skills and proficiency in Microsoft Office tools.
- Able to work independently and collaboratively within a team environment.
- Analytical mindset with an interest in semiconductor manufacturing processes and equipment performance metrics.
Learning Opportunities
- Gain hands-on experience in semiconductor equipment monitoring and FOAK R&D.
- Exposure to data science deployment in a manufacturing environment.
- Interact with cross-functional teams and external vendors to understand real-world equipment performance challenges.
- Learn to implement and analyze automated data collection and visualization systems to support operational decision-making.
This internship is ideal for candidates who are analytical, technically proficient, and interested in applying data science and automation in high-tech manufacturing environments.
