CMOS BEOL Reliability Intern — Defect Analytics & Risk

GlobalFoundries; New York (NY); On-site; USD 28,000 - 55,000; Full time

Job summary

GlobalFoundries in New York is offering an internship in CMOS BEOL Reliability Engineering. The role supports the Fab 8 Reliability Engineering team in developing inline risk metrics from defect data and reliability physics for automotive products.

You will collaborate with cross‑functional teams and present results to stakeholders. Expect mentorship, hands‑on project work, and opportunities to develop dashboards and monitoring tools using Python, Excel, and Power BI.

Qualifications

• Sophomore or higher pursuing a Bachelor's, Master's, or PhD in a relevant field with GPA ≥ 3.0
• English language fluency (written & verbal)
• Ability to work at least 40 hours per week during the internship
• Strong analytical, quantitative and problem‑solving skills
• Ability to work independently while collaborating in a cross‑functional engineering environment

Responsibilities

• Learn BEOL reliability fundamentals, failure mechanisms, inline defect monitoring, reliability risk assessment and FMEA.
• Analyze inline defect data and SEM images to identify defects affecting product reliability.
• Link observed defect types to BEOL degradation mechanisms and assess reliability criticality.
• Perform statistical analysis to identify risk drivers and translate defect density into risk estimates.
• Develop data analysis dashboards to communicate defect trends, risk, and inputs sensitivities.
• Document methods, results, and communicate project status through reports and presentations.

Skills

Analytical skills

Problem solving

Independent work

Team collaboration

Education

Eligible disciplines: Electrical, Materials, Physics, Chemical, Mechanical, Computer, Data Science, Semiconductor Engineering

Tools

Python

JMP

Minitab

Excel

Power BI

MATLAB

Job description

GlobalFoundries in New York is offering an internship in CMOS BEOL Reliability Engineering. The role supports the Fab 8 Reliability Engineering team in developing inline risk metrics from defect data and reliability physics for automotive products.

You will collaborate with cross‑functional teams and present results to stakeholders. Expect mentorship, hands‑on project work, and opportunities to develop dashboards and monitoring tools using Python, Excel, and Power BI.