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Location
Bendemeer, Singapore
Department
Assembly, Process and Equipment Engineering
Project Title
Assembly Output and Yield Optimization for Cost Improvement
Project Description
The Assembly Process and Equipment Engineering Intern will undertake an engineering project focused on improving assembly output, process yield, product quality, and manufacturing cost.
Under the guidance of experienced engineers, the intern will study semiconductor assembly processes and apply engineering methodologies to identify process variations, analyze potential failure modes, and evaluate improvement opportunities. The project will include experimental planning, engineering data collection, statistical analysis, and effectiveness monitoring.
The intern will also explore how data analytics, Generative AI, and Agentic AI can improve engineering analysis, technical documentation, and problem-solving workflows.
Objective of the Project
Identify assembly process factors that influence output, yield, product quality, reliability, and manufacturing cost.
Apply structured engineering methods to investigate selected process or equipment-related opportunities.
Develop and evaluate an improvement proposal using experimental data and statistical analysis.
Explore AI-Enabled methods for accelerating data analysis, identifying patterns, or preparing engineering insights.
Opportunities for Full Time Employment
Interns may be considered for future internship or full-time employment opportunities based on business needs, role availability, academic completion, and demonstrated capabilities.
Project Scope
Study the operating principles and process controls of selected semiconductor assembly equipment under engineer guidance.
Identify and analyze selected assembly process, equipment, or material-related opportunities using engineering data and structured problem-solving methods.
Develop an experimental matrix and conduct controlled evaluations to determine factors affecting assembly output and yield.
Apply data analytics to evaluate results, identify improvement opportunities, and formulate a technical proposal.
Implement an approved project solution in a controlled setting and monitor its effectiveness against defined engineering indicators.
Learning Opportunities
Gain practical knowledge of semiconductor assembly processes, equipment principles, process controls, and product-quality requirements.
Learn how to apply Failure Mode and Effects Analysis, Eight Disciplines problem solving, Design of Experiments, and root-cause analysis.
Develop experience in Statistical Process Control and Fault Detection and Classification methodologies.
Strengthen engineering data analysis, technical documentation, visualization, and presentation skills.
Explore Generative AI, Agentic AI, and AI Assistant tools for data exploration, analysis, workflow improvement, and engineering
productivity.
Deliverables
An experimental plan identifying the process factors, evaluation method, data requirements, and success criteria.
A structured analysis of process, equipment, or material data related to assembly output and yield.
A technical proposal recommending an improvement to the selected assembly process.
An effectiveness-monitoring summary comparing results against the established project indicators.
Impact of the Project
Improve understanding of the process factors influencing assembly output and product yield.
Identify a data-driven opportunity for process, equipment, or material optimization.
Contribute to potential improvements in manufacturing cost, productivity, product quality, or process reliability.
Demonstrate how statistical analysis and AI-Enabled workflows can improve engineering problem-solving efficiency.
Skillsets Required
Positive learning attitude with strong analytical and problem-solving skills.
Basic knowledge of engineering statistics, experimental planning, or Failure Mode and Effects Analysis.
Familiarity with Microsoft Excel and Microsoft PowerPoint for engineering analysis, visualization, and technical communication.
Familiarity with Statistical Process Control, Design of Experiments, or engineering statistical analysis would be advantageous.
Interest in applying Generative AI, Agentic AI, an AI Assistant, or other AI-Enabled workflows to engineering analysis and productivity.
Course of Interest
The ideal candidate should be pursuing a degree or equivalent qualification in Mechanical Engineering, Materials Engineering, Manufacturing Engineering, or a related engineering field.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of 5 months from Jan to May 2027.
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 hrsupport_sg@micron.com
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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