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Project Title:
AI-Enabled Quality Engineering Transformation for Semiconductor Manufacturing
Project Description
Develop quality engineering expertise in semiconductor manufacturing through analytical problem-solving, statistical analysis, quality systems, process characterization, and risk management.
The intern will undertake a structured AI-Enabled transformation project to improve productivity, workflow efficiency, data visibility, and engineering decision-making for contamination control, incoming quality control, and/or chemical laboratory analysis. The project will provide practical exposure to industrial data analytics, Artificial Intelligence, automation, visualization, and cross-functional engineering collaboration.
Objective of the Project
Analyze a defined quality engineering workflow and identify opportunities to improve productivity, efficiency, and decision-making.
Develop an AI-Enabled, automation, or digital solution addressing a real engineering challenge.
Evaluate the solution using appropriate technical, quality, usability, and business-impact criteria.
Build practical knowledge of quality systems, statistical methods, process characterization, and risk management in semiconductor manufacturing.
Opportunities for Full Time Employment
The internship provides exposure to quality engineering career opportunities within semiconductor manufacturing. Interns may be considered for future internships or full-time employment based on business requirements, role availability, project outcomes, and the applicable recruitment process.
Project Scope
Analyze engineering workflows within contamination control, incoming quality control, and/or chemical laboratory analysis.
Develop digital solutions that enhance data visibility, reporting efficiency, and engineering decision-making.
Apply Generative AI, Large Language Models, AI Agents, machine learning, and statistical techniques to enhance inspection strategies, automate quality-data analysis, or improve anomaly detection.
Develop and evaluate a prototype, dashboard, analytical model, Agentic Solution, or automated workflow for the selected use case.
Learning Opportunities
Gain exposure to quality engineering systems and practices within a semiconductor manufacturing environment.
Learn statistical analysis, structured problem-solving, process characterization, and risk-assessment methodologies.
Explore AI-Enabled workflows, Generative AI, AI Agents, data visualization, and software-based or physical automation technologies.
Collaborate with cross-functional engineering teams and subject matter experts.
The internal quality-project example similarly emphasizes semiconductor manufacturing analytics, machine learning, predictive modelling, visualization, and cross-functional collaboration.
Deliverables
An AI-Enabled, automation, or digital transformation solution addressing a defined quality engineering challenge.
Evaluation results demonstrating solution effectiveness, identified limitations, and potential business impact.
Technical documentation covering the methodology, solution design, data requirements, risks, and recommendations.
A final project report and leadership presentation covering the problem statement, methodology, results, business impact, and future scaling opportunities.
The internal internship programme requires interns to present project deliverables to department leaders and includes final reports and presentations within the completion documentation.
Impact of the Project
Improve the visibility, consistency, and accessibility of relevant quality engineering information.
Reduce inefficient or repetitive steps in the selected engineering workflow.
Strengthen anomaly identification, engineering reporting, and data-informed decision-making.
Demonstrate a scalable digital or AI-Enabled approach for future quality engineering transformation.
Skillsets Required
Strong analytical, statistical, and structured problem-solving skills.
Familiarity with Python, R, Structured Query Language, data visualization, or equivalent analytical tools.
Familiarity with machine learning, Generative AI, Large Language Models, AI Assistants, AI Agents, or other AI-Enabled workflows.
Effective collaboration, technical documentation, and presentation skills.
Course of Interest
The ideal candidate should be pursuing a \ degree in Chemical Engineering, Materials Engineering, Electrical and Electronic Engineering, Mechanical Engineering, Manufacturing Engineering, Industrial and Systems Engineering, Data Science, Computer Science, Statistics, Chemistry, Applied Chemistry, or a related field.
Duration of Period
The ideal candidate should be able to commit to a full-time internship period of at least five 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 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.
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