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Automated Defect Classification 2.0
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.
ONTO Dragonfly Autoshell :
1. Draft or update a step‑by‑step user work aid using existing Autoshell procedure references for MMY usage.
2. Compile UAT test notes, screenshots, exceptions, and change notes for engineering/IT review during UAT readiness and implementation monitoring stages.
ADC at Dragonfly and Sonoscan :
1. Complete one ADC model for high runner products each machine model
2. Weekly Model Data Verification : Conduct weekly verification of model data to ensure ongoing accuracy and reliability.
3. Performance Reports : Generate detailed performance reports for each model, highlighting accuracy, precision, recall, and other relevant metrics.
4. User Manuals : Create user manuals and guidelines for the ADC models to assist team members in utilizing the models effectively if necessary.
5. Issue & fix tracking log
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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