Oracle Adaptive Intelligent Apps for Semiconductor Manufacturing
The semiconductor value chain is becoming more complex, with increased time for testing and verification, increased timelines for debugging, and the lack of end-to-end traceability. It is thus crucial for semiconductor manufacturers to constantly reduce scrap, analyze yield & equipment performance, and track & analyze wafer lot composition / genealogy. There is a plethora of data from sensors and enterprise business applications such as MES, ERP, etc., which could help predict a potential business impact. A data-driven approach utilizing Machine Learning / Artificial Intelligence (AI) plays a key role in this context.
Jointly developed by Oracle and LTIMindtree, these SaaS-based analytical applications collect and analyze the Operational Technology (OT), and information technology (IT) data from enterprise business applications using ML / AI techniques, to detect patterns and correlations, thus helping maximize yields and minimize cycle times. They also facilitate end-to-end tracking and analysis of wafer lot composition & genealogy across the various stages of manufacturing process.
Key Highlights
- Embedded data management platform that ingests data from machines and equipment, and enterprise applications such as MES, LIMS, ERP, SCM, HCM and CRM
- Data contextualization and preparation of OT and IT data
- End-to-end model lifecycle management for analyzing KPIs such as yield, quality, cycle time, scrap, etc.
- Predictive analytics helping predict a potential yield loss and downstream risks
- Genealogy and traceability analysis across the end-to-end manufacturing process
Key Benefits
- End-to-end visibility of the manufacturing process
- Rapid root cause analysis, with instant access to pertinent information about material, machine, product, and process
- Actionable insights into operational inefficiencies
- Proactive approach to address potential issues
- Easy identification of impacted products and customers
