À propos de ce poste Intern - F10 QEM Product Quality Engineering Yield Data Analytics chez Micron Technology
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Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.
Department
Quality Engineering and Management
Project Title
AI-Enabled Yield Data Analysis Automation for Semiconductor Memory Testing
Project Description
The intern will undertake a structured project to develop an effective automation solution that improves the efficiency and accuracy of low-yield data analysis in a semiconductor memory test manufacturing environment.
The project will provide exposure to overall test strategy, test operations, manufacturing systems, test methodologies, and lot-disposition processes. The intern will apply programming, data analytics, and Artificial Intelligence tools to analyze engineering information, identify improvement opportunities, and develop a validated analytical workflow.
Objective of the Project
Develop an automated method that improves the efficiency, accuracy, and consistency of low-yield data analysis.
Build an understanding of semiconductor memory functionality, test strategy, test methodology, and lot-disposition processes.
Apply programming, analytics, and AI-Assisted tools to a defined engineering problem.
Evaluate the developed solution against agreed technical and project objectives.
Opportunities for Full Time Employment
Interns may be considered for future internship or full-time employment opportunities based on business requirements, role availability, and the applicable recruitment process.
Project Scope
Learn the overall test strategy, test operations, and manufacturing processes used in semiconductor memory testing.
Become familiar with the systems, applications, hardware, and data flows used within the test manufacturing environment.
Study test methodology and lot-disposition processes used for low-yield analysis.
Develop and evaluate an AI-Enabled automation solution that improves data-analysis efficiency and accuracy.
Learning Opportunities
Gain practical exposure to semiconductor memory devices, product testing, yield analysis, and test manufacturing processes.
Learn how engineering systems and applications are used to investigate low-yield conditions and inform lot-disposition decisions.
Develop experience in programming, data analytics, visualization, workflow automation, and AI-Assisted engineering analysis.
Collaborate with cross-functional engineering teams and subject matter experts throughout the project lifecycle.
Deliverables
A documented analysis of the selected low-yield analysis workflow, including requirements, data inputs, process gaps, and improvement opportunities.
A functional automation or analytics solution that improves the efficiency and accuracy of the selected analysis process.
Validation results comparing the existing and proposed analytical approaches, including identified limitations and recommendations.
A final project report and presentation covering the problem statement, methodology, solution, results, learning outcomes, and potential future enhancements
Impact of the Project
Improve the efficiency, accuracy, and consistency of engineering data analysis.
Enable clearer identification and investigation of low-yield conditions.
Strengthen data-informed engineering decision-making through automation and visualization.
Contribute analytical insights relevant to next-generation storage applications used in data centres.
Skillsets Required
Familiarity with programming, scripting, or analytics tools such as Python, Perl, Tableau, Microsoft Power BI, or equivalent platforms.
Strong analytical, structured problem-solving, data-interpretation, and time-management skills.
Effective written and verbal communication skills, with the ability to document and present technical findings.
Familiarity with Artificial Intelligence tools, AI Assistants, machine learning, or AI-Enabled workflows, with sound judgement regarding organizational and legal requirements.
Course of Interest
The ideal candidate should be pursuing a degree in Electrical Engineering, Electronic Engineering, Microelectronics Engineering, Materials Engineering, Data Science, Computer Science, or a related field.
Duration of Period
The ideal candidate should be able to commit to a full time internship period of[ t least 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 life for 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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