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Semiengineering
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Improving Fab Engineering Efficiency With Autonomous Data Analytics
- As a process integration engineer, identifying yield enhancement opportunities involved analyzing relationships between bin failures and process parameters within the fab.
- Challenges included integrating various data types like sort maps, electrical test maps, parametric data, in-line metrology, and defect scans.
- Efficiency in analyzing quality excursions is crucial due to the impact on tool utilization and fab shipments.
- Synopsys' Decision Support System (DSS) efficiently analyzes diverse data sources stored in a data lakehouse, identifying latent relationships among different data types.
- DSS provides ranked lists of data behaviors, prioritizing higher-correlating data that could be of interest to users.
- The value of DSS lies in correlating map patterns and identifying relationships between different data parameters in the semiconductor industry.
- DSS handles large data volumes, facilitating quick analysis and reducing manual work during investigations.
- The Subscription feature in DSS notifies users of new data models or behaviors based on their set keywords, aiding in timely insights.
- Increasing complexity in semiconductor fabs necessitates advanced data management approaches, making autonomous data analytics crucial for fab engineers.
- The Decision Support System by Synopsys enhances fab engineering efficiency by identifying latent behaviors, correlating data, and presenting results effectively.
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