At a glance

Location: Fab 10A, Singapore
Work type: Onsite

What they are looking for

PythonData analyticsDashboard developmentData visualizationStatistical analysis

Location: Fab 10A, 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

Heterogeneous Integration Group, High Bandwidth Memory Product System Engineering

Project Title

Artificial Intelligence-Enabled High Bandwidth Memory Defect Library and Analytics Dashboard Development

Project Description

High Bandwidth Memory products generate significant volumes of engineering, electrical test, and failure analysis data throughout the manufacturing and test lifecycle. Consistent documentation and structured management of defect signatures are important for strengthening engineering knowledge retention, improving information accessibility, and enabling data-driven technical analysis.

This internship project focuses on developing a structured High Bandwidth Memory Defect Library that captures the electrical and physical signatures of selected defect modes across multiple test flows. The intern will apply engineering, data analytics, Python programming, and dashboard-development concepts to organize defect information and present it through an interactive visualization platform.

Under the guidance of experienced engineers, the intern will also evaluate appropriate Artificial Intelligence-enabled approaches, including Generative Artificial Intelligence, Artificial Intelligence Assistants, Agentic Artificial Intelligence solutions, and Large Language Model-based tools. These approaches may be used to improve defect categorization, information retrieval, engineering knowledge management, and insight generation.

The assignment is a supervised, educational, and project-based internship. It is intended to develop the intern’s technical and professional capabilities and does not replace the responsibilities of regular employees.

Objective of the Project

Establish a standardized High Bandwidth Memory Defect Library that documents the electrical and physical signatures of selected defect modes.

Develop an interactive dashboard for visualizing, tracking, and analyzing defect information across multiple test flows.

Create a consistent data structure and categorization framework that strengthens engineering knowledge retention and information retrieval.

Evaluate feasible Artificial Intelligence-enabled methods for improving defect classification, searchability, and technical insight generation.

Opportunities for Full Time Employment

Strong internship performance may provide opportunities for consideration for future full-time employment. Any employment consideration will be subject to business requirements, position availability, candidate suitability, and the applicable recruitment process.

Project Scope

The intern will undertake the following project-based activities under appropriate technical guidance:

Study the electrical and physical signatures associated with selected semiconductor defect modes and examine how these signatures appear across different process stages and test flows.

Organize engineering and failure analysis information into a structured, searchable, and reusable defect knowledge repository.

Develop an interactive dashboard for standardized defect categorization, visualization, trend analysis, and technical reporting.

Apply Python, statistical methods, and data analytics techniques to examine relationships between physical defect characteristics and electrical test results.

Evaluate Artificial Intelligence-enabled methods, including Generative Artificial Intelligence, Artificial Intelligence Assistants, Agentic Solutions, and Large Language Models, for defect categorization, information retrieval, and engineering knowledge management.

Learning Opportunities

Through this internship project, the successful candidate will have opportunities to:

Develop knowledge of High Bandwidth Memory technology, semiconductor product engineering, manufacturing processes, and defect mechanisms.

Learn electrical testing methodologies, failure signature interpretation, defect characterization, and failure analysis concepts.

Apply Python programming, statistical analysis, data management, dashboard development, and data visualization to an engineering project.

Explore responsible applications of Artificial Intelligence, Generative Artificial Intelligence, Artificial Intelligence Assistants, Agentic Artificial Intelligence, and Large Language Models in engineering workflows.

Strengthen technical documentation, engineering communication, presentation, and cross-functional collaboration skills.

Deliverables

At the completion of the internship, the expected project deliverables include:

A structured and searchable High Bandwidth Memory Defect Library containing documented electrical and physical signatures for selected defect modes.

An interactive and user-friendly dashboard that enables standardized visualization, tracking, filtering, and analysis of defect information across multiple test flows.

A documented defect categorization framework, data structure, and methodology that enable consistent future knowledge capture.

A technical report describing the project methodology, analytical observations, dashboard functionality, limitations, and recommended future enhancements.

A final presentation and demonstration communicating the project outcomes, engineering insights, and potential Artificial Intelligence-enabled applications.