Metro
Technology
Intern
Neural analysis suggests this role is
optimal for Entry candidates.
“Intern at Metro. Skills: Machine learning, Defect detection. Train advanced ADC models. Validate model performance”
What You'll Achieve.
Improve accuracy of defect detection; Improve efficiency of defect detection; Reduce human error; Improve inspection quality; Ensure consistent defect classification; Ensure reliable defect classification; Ensure models meet performance standards
Industry & Context.
Problem-solving skills; Analytical skills
What They're Looking For.
Must Have
Currently pursuing a degree in Metrology, Engineering, Materials Science, or related field, Analytical and problem-solving skills, Ability to interpret complex data, Ability to derive actionable insights, Excellent written communication skills, Excellent verbal communication skills, Ability to work collaboratively in a team environment, Ability to contribute to project success
What You'll Do.
Train advanced ADC models
Validate model performance
Automate defect identification
Automate defect categorization
Engage with multiple teams
Gather diverse expertise
Gather diverse insights
Use RDAViewer for data verification
Verify defect data accuracy
Ensure training dataset integrity
Ensure models meet performance standards
Integrate ADC models across platforms
Provide user training
Train team members on ADC models
Maintain documentation
Document model training process
Document data verification
Document analysis procedures
Compile UAT test notes
Complete ADC model for products
Conduct weekly model data verification
Ensure ongoing accuracy
Ensure ongoing reliability
Generate performance reports
Create user guidelines
Update MAM process flow
Integrate new ADC models
How You'll Work.
Team & Collaboration
Multiple Teams and Departments; Engineering, IT, QA, SMAI; Cross-functional teams
Communication Scope
Written communication; Verbal communication
Full Job Description
**Our vision is to transform how the world uses information to enrich life for _all_. ** Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. ## Project Title Automated Defect Classification 2.0 ## Description The Automated Defect Classification (ADC) 2.0 project aims to improve the accuracy and efficiency of defect detection across multiple inspection platforms, including ATI Wind, Dragonfly, and Sam Scan. The primary goal is to train advanced ADC models and validate their performance to ensure consistent and reliable defect classification in the production line. This involves using brand new machine learning techniques to automate the identification and categorization of defects, thereby reducing human error and improving overall inspection quality. ## Scope * Engage with Multiple Teams and Departments : Collaborate with various teams, including Engineering, IT, QA, and SMAI, to gather diverse expertise and insights for model training. * Use RDAViewer for Data Verification : Apply RDAViewer to verify the accuracy of defect data and ensure the integrity of the training dataset. * Data Collection and Analysis for Qualification : Collect and analyze defect data to qualify the ADC models, ensuring they meet the required performance standards. * Model Optimization : Continuously refine and optimize the ADC models based on feedback and performance metrics. * Cross-Platform Integration : Ensure seamless integration of ADC models across ATI Wind, Dragonfly, and Sam Scan platforms. * User Training : Provide training sessions for team members on how to use and interpret the ADC models. * Documentation : Maintain comprehensive documentation of the model training process, data verification, and analysis procedures. ## Supporting Team * Engineering team * IT Team * QA Team * SMAI Team * Manufacturing Team ## Deli
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