Intern - Manufacturing Technician (Dry Etch)

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• Summary: Applying automation software to improve productivity of the equipment engineering team. Learn about the importance of equipment parameters to wafer processing and apply data analytics on them for monitoring and deviation detection.
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• Department: Manufacturing Dry Etch
  
• Project Title: Developing Automation Solutions to Improve Productivity and Enhancing Deviation Detection in Equipment Engineering
  
• Project Description:
  This project focuses on the design and implementation of automation solutions to enhance productivity within the equipment engineering function in a semiconductor manufacturing environment. The intern will work closely with engineering stakeholders to understand equipment processes, key parameters influencing wafer fabrication, and current operational challenges that can be improved through automation.
  

  The intern will gain exposure to different automation platforms and data systems available within the organization, and learn how these tools are applied to streamline workflows and reduce reliance on manual data handling. In addition, the intern will explore the relationship between equipment parameters and process outcomes, and apply data analytics methods to monitor performance and identify deviations.
  

  Through this structured project, the intern will develop both technical and analytical capabilities while contributing to a defined improvement initiative within the engineering domain

  Objective of the Project
  • Develop automation solutions that improve engineering workflow efficiency
  • Apply data analytics techniques to equipment parameters for monitoring and deviation detection
  • Build understanding of equipment behaviour and its impact on wafer processing outcomes
• Scope:
  • Design and implement automation solutions using available engineering tools and platforms
  • Develop automated data extraction, transformation, and visualization workflows
  • Analyze equipment parameters and evaluate correlations across datasets
  • Apply analytical techniques to study equipment matching and performance trends
  • Identify opportunities to enhance process visibility and target="_blank">hrsupport_sg@micron.com

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