Intern - Manufacturing Dry Etch

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Location

1 Woodlands Industrial Park D, Street 1, Singapore

Department

Dry Etch Manufacturing

Project Title

Predictive Modelling of Dry Etch Equipment Consumable-Part Lifetime for Cost and Productivity Improvement

Project Description

The intern will undertake a structured engineering project focused on identifying key consumable parts in Dry Etch equipment and studying the process and equipment parameters that influence their useful lifetime.

Under the guidance of experienced engineers, the intern will analyze equipment, process, maintenance, and consumables-related data to develop a predictive analysis model for estimating part lifetime. The project aims to improve consumable utilization and cost efficiency while maintaining safety, product quality, equipment performance, and manufacturing yield.

The project will provide exposure to Dry Etch equipment, lean manufacturing, cost management, data analytics, predictive modelling, dashboard development, and AI-Enabled engineering workflows.

Objective of the Project

• Develop an understanding of Dry Etch equipment functions, processes, and key consumable parts.
• Identify equipment and process parameters that influence consumable-part lifetime.
• Develop a predictive methodology for estimating the useful lifetime of selected parts.
• Generate data-based recommendations that may improve cost efficiency and productivity while protecting safety, quality, and yield.

Opportunities for Full-Time Employment

High-performing interns may be considered for future internship or full-time employment opportunities, subject to business requirements, position availability, and the applicable selection process.

Project Scope

• Study selected Dry Etch equipment functions, processes, consumable parts, and relevant replacement criteria.
• Analyze equipment, process, maintenance, logistics, and cost data to identify performance trends and part-lifetime relationships.
• Develop and evaluate a predictive analysis model for estimating the useful lifetime of selected consumable parts.
• Build dashboards or visualizations using tools such as Microsoft Excel, Power BI, Python, Structured Query Language, or approved AI-Enabled platforms.
• Document analytical findings and develop recommendations for improving consumable utilization, cost efficiency, productivity, and engineering decision-making.

Learning Opportunities

• Gain practical knowledge of Dry Etch equipment functions, processes, and consumable-part behaviour.
• Develop an understanding of logistics and cost management in a lean manufacturing environment.
• Gain experience in manufacturing data analytics, predictive modelling, anomaly detection, and dashboard development.
• Collaborate with professionals from Manufacturing, Engineering, Quality, Automation, and Data Science.

Deliverables

• A documented analysis of selected Dry Etch equipment consumables and the factors that influence their lifetime.
• A predictive analysis model or methodology for estimating the useful lifetime of selected parts.
• A dashboard, visualization, or analytical report presenting relevant trends, model outputs, and findings.
• Data-based recommendations and a final technical presentation covering the methodology, results, limitations, and potential next steps.

Impact of the Project

• Improve understanding of consumable-part lifetime and usage patterns in selected Dry Etch equipment.
• Identify potential opportunities to improve consumable utilization, cost efficiency, and equipment productivity.
• Enhance visibility into equipment and process trends while protecting safety, product quality, and manufacturing yield.

Skillsets Required

• Basic proficiency in Microsoft Excel and PowerPoint.
• Strong analytical, problem-solving, communication, and technical-documentation skills.
• Familiarity with data analytics or visualization tools such as Python, Power BI, or Structured Query Language is advantageous.
• Interest in Artificial Intelligence, Machine Learning, predictive analytics, or AI-Enabled engineering workflows.
• An inquisitive mindset and willingness to learn semiconductor equipment, manufacturing processes, and cost-management concepts.

Course of Interest

The ideal candidate should be pursuing a degree or equivalent qualification in Mechanical Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Data Analytics, Computer Engineering, or a related engineering discipline.

Duration of Period

The ideal candidate should be able to commit to a full-time internship period ofat least 5 months.

About Micron Technology, Inc.

We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich lifefor all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities - from the data center to the intelligent edge and across the client and mobile user experience.

To learn more, please visit micron.com/careers

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

To request assistance with the application process and/or for reasonable accommodations,please contact [email protected]

Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.

Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.

AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.

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