Intern- MSB Process Integration Engineer

Singapore

Department

MSB Process Integration Engineering

Project Title

Semiconductor Manufacturing Reject Reduction Through Process Variability Analytics

Project Description

The intern will participate in a structured project focused on improving semiconductor manufacturing reject rates through process variability reduction.

The project will provide practical exposure to semiconductor packaging, Assembly and Test processes, and manufacturing data analytics. Under appropriate guidance, the intern will analyze inline signals, process parameters, and test results to identify patterns and potential contributors to process variability and yield loss.

The intern will also explore the responsible application of Artificial Intelligence and AI-Enabled analytical tools to improve the efficiency, quality, and effectiveness of project analysis.

Objective of the Project

The project aims to:

• Identify potential sources of process variability and yield loss.

• Improve the analysis of inline signals, process data, and test results.

• Determine relationships between process conditions and manufacturing rejects.

• Develop data-driven recommendations for engineering evaluation.

• Explore AI-Enabled approaches for manufacturing data analysis.

Consideration for future internship or full-time employment opportunities will be subject to business needs, position availability, and the applicable selection process.

Project Scope

The intern will be given the opportunity to:

• Learn about semiconductor packaging, Assembly and Test processes, and common sources of process variability and yield loss.

• Analyze inline signals, process parameters, manufacturing information, and test results using appropriate analytical tools.

• Apply statistical and data analytics techniques to identify trends, correlations, and potential contributors to reject rates.

• Evaluate AI-Enabled tools and insights that may enhance project analysis while complying with organizational and legal requirements.

• Document the methodology, findings, limitations, and recommended next steps for engineering review.

Learning Opportunities

The intern will have the opportunity to:

• Gain practical exposure to semiconductor packaging and high-volume manufacturing.

• Learn how process variability and yield loss are investigated using manufacturing and test data.

• Apply programming, statistical analysis, visualization, and structured problem-solving techniques.

• Build proficiency in the responsible use of Artificial Intelligence and AI-Enabled analytical workflows.

• Strengthen technical documentation, stakeholder communication, and cross-functional collaboration skills.

Deliverables

The intern is expected to deliver:

• A structured analysis of relevant inline signals, process parameters, and test results.

• Identification of key patterns and potential contributors associated with process variability and reject rates.

• Analytical models or visualizations that communicate findings clearly.

• Documented recommendations and proposed next steps for engineering evaluation.

• A final technical report and presentation covering the methodology, findings, limitations, and recommendations.

Impact of the Project

The project is intended to:

• Improve visibility into manufacturing variability and potential sources of yield loss.

• Enhance data analysis for semiconductor Assembly and Test concerns.

• Provide data-driven insights for process stability and quality improvement.

• Strengthen the application of advanced analytics and AI-Enabled workflows in manufacturing.

• Establish reusable analytical methods for future variability-reduction projects.

Skillsets Required

The ideal candidate should possess:

• Programming knowledge in Python, Structured Query Language, or equivalent analytical tools.

• Strong analytical, problem-solving, systems-thinking, and critical-thinking capabilities.

• Familiarity with statistics, data analysis, visualization, Artificial Intelligence, or AI-Enabled workflows.

• The ability to learn quickly, investigate technical topics, and collaborate effectively across teams.

• Strong self-motivation, outcome orientation, interpersonal communication, and technical documentation skills.

Course of Interest

The ideal candidate should be pursuing a bachelor’s degree in Mechanical Engineering, Electrical Engineering, Chemical Engineering, Materials Engineering, Industrial Engineering, Computer Engineering, or a related engineering field.

Duration of Period

The ideal candidate should be able to commit to a full time internship period of 5 months from Jan to May 2027.