Intern - F10 QEM Product Quality Engineering Yield Data Analaytics

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.