Intern - NAND Probe Product Engineering (Test Mode Integration)

Location: Fab 10N/X, Singapore

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.

Location

Singapore

Department

NAND Probe Product Engineering

Project Title

AI-Enabled Test Mode Innovation and Integration

Project Description

The NAND Probe Product Engineering Intern will undertake a structured learning and innovation project focused on test mode development concepts, silicon validation methodologies, and AI-Enabled engineering solutions for advanced NAND technologies.

Working alongside experienced Product and Design Engineers, the intern will develop knowledge of NAND test methodologies, Design for Test (DFT) concepts, and silicon validation processes. This internship combines semiconductor engineering fundamentals with Artificial Intelligence, data analytics, automation, and Generative AI technologies, providing an opportunity to explore how AI-Enabled tools and Agentic AI solutions can enhance engineering learning, technical knowledge discovery, test effectiveness, and innovation.

Objective of the Project

The intern will have the opportunity to

Develop an understanding of NAND architecture, probe testing concepts, Design for Test methodologies, and test mode integration practices.

Explore engineering datasets to identify product behaviours, yield learning opportunities, and failure patterns using statistical and analytical techniques.

Investigate approaches to improve test observability, diagnostics, test coverage, and engineering learning efficiency.

Apply Artificial Intelligence, Machine Learning, automation, and Generative AI techniques to engineering use cases.

Formulate recommendations that contribute to future engineering innovation, technical knowledge development, and decision-making processes.

Opportunities for Full Time Employment

Successful internship completion may provide opportunities for consideration for future full-time roles within Micron, subject to business needs, individual performance, academic qualifications, and available openings.

Project Scope

The intern will undertake a structured learning project that may include:

Exploring NAND architecture, product features, test algorithms, and Design for Test capabilities used in advanced memory technologies.

Examining methodologies for test mode development, integration, silicon validation, and product characterisation.

Investigating engineering data analysis, visualisation, and automation techniques using Python, Machine Learning, and modern analytics tools.

Developing proof-of-concept AI Assistant, Agentic AI, or Large Language Model (LLM)-based solutions that enhance engineering knowledge retrieval, documentation, analytical workflows, or innovation activities.

Evaluating technical observations and proposing improvement opportunities related to product learning, diagnosability, and engineering efficiency.

Learning Opportunities

Through this internship, participants will gain exposure to:

NAND Product Engineering, semiconductor testing methodologies, and silicon validation concepts.

Design for Test integration and test mode innovation within advanced memory technologies.

Statistical analysis, engineering problem-solving methodologies, and data-driven decision making.

Python programming, data engineering, automation, and visualisation techniques.

Artificial Intelligence, Machine Learning, Generative AI, Agentic AI, and AI-Enabled engineering workflows.

Deliverables

By the conclusion of the internship, the intern is expected to produce:

A technical assessment summarising observations related to test modes, validation methodologies, feature effectiveness, and potential improvement opportunities.

Analytical dashboards, visualisation tools, automation prototypes, or data exploration solutions developed for engineering learning purposes.

An AI-Enabled proof-of-concept, Machine Learning model, AI Assistant, Agentic AI solution, or workflow automation prototype developed for learning and innovation purposes.

A technical report and presentation documenting project objectives, methodology, findings, recommendations, limitations, and future exploration opportunities.

Impact of the Project

The project is intended to

Demonstrate practical applications of Artificial Intelligence and automation within Product Engineering environments.

Enhance engineering productivity through improved information access, data analysis, and workflow efficiency.

Contribute reusable concepts, methodologies, and learning resources that may support future technology development and engineering innovation.

Strengthen understanding of AI-Enabled engineering practices for next-generation semiconductor development.

Skillsets Required

Understanding of semiconductor devices, electronics, digital systems, integrated circuit testing, or related engineering disciplines.

Programming knowledge in Python, MATLAB, or similar languages demonstrated through coursework, laboratory assignments, or project experience.

Familiarity with Artificial Intelligence, Large Language Models (LLMs), Machine Learning, workflow automation, AI Assistants, or Agentic AI concepts.

Strong analytical, critical thinking, and problem-solving capabilities with a demonstrated willingness to learn.

Effective communication and collaboration skills in a multidisciplinary engineering environment.

Course of Interest

The ideal candidate should be pursuing a Bachelor's, Master's, or PhD degree in Electrical Engineering, Electronics Engineering, Computer Engineering, Computer Science, Data Science, Art