Yield Defect Metrology Engineering Intern

Intel is hiring a Yield Defect Metrology Engineering Intern. Level rates it ; you can apply on Intel.

Level's read on this job

Little AI ●○○○ is how Level rates this role: aI is not part of the work.
Level classes it as an internship.
Intel has 864 other open roles on Level.
Posted in the last few days.

AI in this role

Job Details:

Job Description:

Position Overview As a Yield Defect Metrology Engineering Intern, you will work alongside engineering teams to support yield improvement and defect reduction initiatives. You will gain firsthand experience in semiconductor manufacturing by analysing defect data, monitoring process performance, and participating in engineering investigations. This internship provides exposure to advanced manufacturing technologies, statistical data analysis, defect engineering, and process optimization. You will have opportunities to apply engineering concepts to real manufacturing challenges while developing analytical, technical, and problem-solving skills that are highly valued in the semiconductor industry. Key Responsibilities Yield Monitoring and Manufacturing Analytics • Support engineers in monitoring manufacturing performance indicators, defect trends, and process health metrics. • Assist in reviewing inspection and metrology data to identify unusual patterns and potential yield concerns. • Learn how Statistical Process Control (SPC) is applied in semiconductor manufacturing environments. • Participate in daily operational reviews and yield learning discussions. Data Analytics and Visualization • Analyze large manufacturing datasets using tools such as Microsoft Excel, Power BI, Python, or other analytics platforms. • Support development of dashboards, reports, and visualizations to improve engineering decision-making. • Assist in identifying defect trends, recurring issues, and potential yield improvement opportunities. • Present analysis findings to engineering mentors and stakeholders. Defect Analysis Support • Review and organize defect data generated from automated inspection systems. • Assist engineers in defect classification validation and data quality improvement activities. • Support investigations into defect excursions and manufacturing abnormalities. • Learn defect analysis methodologies commonly used in semiconductor manufacturing. Engineering Project Support • Participate in yield improvement and cost-reduction projects. • Support process improvement initiatives aimed at reducing defectivity and improving manufacturing performance. • Assist in data collection, analysis, documentation, and presentation of project outcomes. • Contribute to continuous improvement activities using structured problem-solving methods. Automation and Digitalization • Support development of data automation, reporting, and process-monitoring solutions. • Assist in creating scripts, dashboards, or visualization tools to improve operational efficiency. • Explore opportunities to leverage data science, machine learning, or automation techniques in manufacturing environments. • Learn how engineering data systems are used to drive operational excellence. Cross-Functional Collaboration • Collaborate with engineers, technicians, and manufacturing teams on assigned projects. • Participate in technical discussions and engineering reviews. • Gain exposure to interactions between Yield, Process Integration, Metrology, Manufacturing, and Quality organizations. • Prepare project updates and communicate findings effectively.

Qualifications:

Requirements: • Strong interest in semiconductor manufacturing, data analytics, or engineering problem-solving. • Good analytical and critical-thinking skills. • Strong attention to detail and willingness to work with large data sets. • Effective communication and teamwork abilities. • Self-motivated and eager to learn in a fast-paced manufacturing environment. Qualifications: Minimum Qualifications: • Bachelor's degree in a specialized field (e.g., Electrical Engineering, Mechanical Engineering, Materials Science, or related discipline). • Able to commit to a minimum internship period of 6 months. Preferred Qualifications • Familiarity with Microsoft Excel, Power BI, MATLAB, Python or other data analysis tools. • Basic understanding of statistics and data visualization. • Coursework or project experience involving data analysis, manufacturing, process control, or automation. • Strong presentation and communication skills. • Demonstrated curiosity and passion for solving technical challenges.

Job Type:

Student / Intern

Shift:

Shift 1 (Malaysia)

Primary Location:

Malaysia, Penang

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Work Model for this Role

This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

How we rate this

Yield Defect Metrology Engineering Intern at Intel rates 18 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.