Intern - Test Equipment Engineer Posted ago

Location

Micron Singapore Back End

Test Engineering

Machine Availability and Parts per Jam Improvement Through Predictive Analytics

The intern will undertake an engineering project to improve burn-in equipment performance using predictive analytics, data automation, and Agentic AI. Under engineer guidance, the intern will analyze equipment alarm logs, investigate downtime patterns, and develop data-driven solutions to improve machine availability and Parts per Jam performance.

• Develop a structured alarm database for selected equipment, burn-in boards, and sockets.
• Apply predictive analytics to identify conditions linked to downtime or reduced performance.
• Develop automated notification and visualization workflows.
• Evaluate Agentic AI applications for equipment monitoring and alarm analysis.

Interns may be considered for future internship or full-time employment opportunities based on business needs, role availability, academic completion, and demonstrated capabilities.

• Learn the operating principles, alarm structures, and performance indicators of selected burn-in equipment.
• Collect, clean, structure, and validate equipment alarm-log data.
• Develop trigger logic and automated notifications for selected JTS equipment, burn-in board, and socket alarms.
• Apply statistical and predictive methods to identify factors affecting availability, downtime, and Parts per Jam.
• Evaluate the feasibility of socket-masking automation and relevant Agentic AI solutions.

• Gain practical knowledge of semiconductor test processes and burn-in equipment.
• Develop experience with Python, Structured Query Language, Microsoft Power BI, and Microsoft Power Automate.
• Learn predictive analytics, data visualization, trigger validation, and performance monitoring.
• Explore Generative AI, Microsoft Copilot, and Agentic AI for engineering automation.

• Validated alarm database with defined trigger logic.
• An automated notification and visualization workflow.
• A predictive methodology for identifying equipment downtime patterns.
• A socket-masking automation feasibility study.
• An effectiveness analysis covering machine downtime and Parts per Jam performance.

• Improve visibility of equipment alarm and downtime patterns.
• Enable earlier identification of equipment conditions affecting availability.
• Identify opportunities to improve Parts per Jam and reduce downtime.
• Demonstrate AI-Enabled Test Engineering workflows.

• Familiarity with Python, Structured Query Language, Microsoft Power BI, or Microsoft Power Automate.
• Basic knowledge of statistics, predictive analytics, and data visualization.
• Familiarity with Generative AI, Microsoft Copilot, Agentic AI, or AI-Enabled workflows.
• Strong analytical, systems-thinking, problem-solving, and communication skills.
• Ability to learn quickly, investigate technical issues, and communicate findings clearly.

The ideal candidate should be pursuing a degree in Electrical and Electronic Engineering, Computer Engineering, Data Science, Mechatronics Engineering, or a related technical field.

The ideal candidate should be able to commit to a full time internship period of at least 6 months, from January 2027 to June 2027.

We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron®…