Intern - F10 QEM Product Quality Engineering Yield Data Analaytics

Our vision is to transform how the world uses information to enrich life for all. 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,