Semiconductor Process Intern: Thermal Optimization & AI

Micron Technology; Singapore; On-site; SGD 20,000 - 33,000; Full time

Job summary

Micron Technology, Singapore, invites a bachelor’s or master’s student for a full-time Process Engineering internship focused on wafer thermal management and plasma strip optimization. You will conduct structured experiments, apply DOE, and develop data-driven insights under experienced engineers.

Responsibilities include analyzing chuck temperature effects, building predictive analytics models, and delivering actionable recommendations to improve robustness, yield, and cost performance.

Qualifications

• Strong analytical, experimental and problem-solving skills.
• Familiarity with statistical analysis, DOE, data visualization, or process modeling.
• Exposure to Python, JMP, Minitab, Power BI or similar tools is advantageous.
• Pursuing a bachelor’s or master’s degree in engineering or related field.

Responsibilities

• Plan and conduct experiments to study chuck temperature effects on wafer thermal behavior and process performance.
• Apply Design of Experiments and statistical methods to identify key temperature relationships.
• Develop data-based optimization recommendations to improve robustness, yield, productivity, or cost performance.
• Deliver a thermal-characterization and DOE study report and a predictive analytics approach.

Skills

Analytical thinking

Experimentation

Statistics

Data visualization

Communication

Machine Learning interest

Education

Bachelor or Master in Engineering/Science

Tools

Excel

Python

JMP

Minitab

Power BI

Job description

Micron Technology, Singapore, invites a bachelor’s or master’s student for a full-time Process Engineering internship focused on wafer thermal management and plasma strip optimization. You will conduct structured experiments, apply DOE, and develop data-driven insights under experienced engineers.

Responsibilities include analyzing chuck temperature effects, building predictive analytics models, and delivering actionable recommendations to improve robustness, yield, and cost performance.