Date: 26 October 2021
Time: 2:00PM - 3:00PM (Singapore) | 11:30AM - 12:30PM (India)
Presenter: Yulia Wunyuti, Manager Fab7 Statistical Process Control & Lean Six Sigma Black Belt at GlobalFoundries Singapore
Location: Zoom Live Webinar
Practical Workflow Sharing: Machine Learning Applications in Solving Random Low Yield Cases in Wafer Foundries
- From data preparation to model fitting, big data presents huge challenges; but if done right, yield rewarding results. -
The volume and complexity of data in semiconductor wafer foundries are significant. Semiconductor manufacturing processes consist of hundreds of individual steps, with several additional tests for defective parts identification.
These sophisticated processes translate into big and messy data from multiple sources, such as Inline Measurement, Process Equipment, Electrical Test (ET) and Wafer Sort Yield Data etc.
Troubleshooting or attempts to explain yield variation is a huge challenge for yield engineers as the complexity exponentially increases with amount of data available.
With 19 years industrial experiences, Yulia will share how she utilizes a combination of tools to achieve a practical yet efficient workflow in analyzing yield data.
In this 1-hour webinar, you will learn how to effectively analyze random low yield issue with big data.
Hear from Yulia from GlobalFoundries Singapore with background in yield ramping, Process Integration, Product Engineering and Fab Quality System.
- Data preparation & preliminary screening techniques
- Predictive modeling techniques for root-cause analysis
- Model fitting and validation
- Practical case studies
Who is this webinar for?
Engineers working in the Manufacturing Sector, Semiconductor, High-Tech, R&D department who wants to improve efficiency and effectiveness of yield analysis, especially relevant to:
- Process Engineer / Process Integration Engineer (PI)
- New Process Integration Engineer (NPI)
- Product Engineer (PE) / Yield Enhanced Engineer (YE)
- Product and Testing Engineer (PTE)
- Quality Engineers (QE)
Remark: No recording will be shared for this webinar.
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