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Department:
Post Electrical Process Engineering
Project Title:
Machine Drift Detection Using Advanced Data Analytics and Machine Learning
Project Description:
Semiconductor manufacturing processes generate large volumes of machine and inspection data that can be leveraged to improve product quality and process stability. This project focuses on the development of advanced data analytics and machine learning approaches to identify early signals of machine-related quality variations.
The objective of this project is to design and develop an end-to-end analytics solution using machine and measurement data, covering data extraction, preprocessing, modeling, and result visualization to enable earlier detection of potential quality issues.
Objective of the Project:
- Develop understanding of machine data structures and quality-related signals in semiconductor manufacturing
- Apply data analytics and machine learning techniques to identify patterns associated with process variation
- Gain exposure to end-to-end solution development, from data processing to visualization
Project Scope:
- Design and implement data processing workflows for machine log, inspection, and measurement datasets
- Develop machine learning models for feature selection, pattern recognition, and drift detection
- Evaluate modeling approaches to identify early indicators of potential quality variation
- Analyze relationships across multiple variables to understand process behavior and optimization opportunities
- Develop visualization of analytical results to facilitate interpretation and insights
- Document data workflows, model design, and analytical findings for knowledge transfer
Learning Opportunities:
- Exposure to industrial data analytics and machine learning applications
- Experience in handling large-scale manufacturing datasets
- Understanding of modeling, simulation, and optimization techniques
- Opportunity to work on real-world engineering problems and translate data insights into meaningful outputs
- Interaction with engineering subject matter experts to understand process and data context
Deliverables:
- Data processing and analytics workflow for machine and inspection datasets
- Machine learning model for drift detection and pattern identification
- Analytical outputs and visualization of model results
- Documentation of methodology, model development, and key findings
Impact of Project:
This project contributes to improved detection of process variation signals and enables data-driven insights for enhancing product quality and process stability.
Skillsets Required:
- Basic knowledge of programming (Python or equivalent)
- Understanding of data analytics and machine learning concepts
- Analytical thinking and problem-solving skills
- Familiarity with data processing and statistical analysis is advantageous
Course of Interest:
- Computer Science
- Data Science / Data Engineering
- Engineering (Electrical, Mechanical, Industrial, or related fields)
- Statistics or Applied Mathematics
Duration Period:
Minimum 5 months (Semester Internship from September 2026 to February 2027)
About Micron Technology, Inc.
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® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.
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