- Regulatory changes
- Customer demand management
- Traceability
- Adaptive process control with the inclusion of consumables
- SubFab Optimization
- Capacity sharing between partners amid disruptions
- Yield Improvement through data-driven external services
- Life Cycle Assessment (LCA) methodology with the underlying Life Cycle Inventory (LCI)
- Optimization of environmental key figures
- Product Carbon Footprint (PCF)
SEMICONDUCTOR-X
Use cases
Data ecosystem Description
SEMICONDUCTOR-X aims to make the European semiconductor industry sustainable and resilient through comprehensive digitalization and the establishment of traceability. Together with our partners, we are developing new data-driven business models and encouraging innovation along the entire value chain.
Industry:
Semiconductors
Region:
DACH, United States, East Asia, Southeast Asia (ASEAN)
Regulatory changes
Constantly evolving import and export regulations and new standards require flexible adaptation of production processes and systems.
By leveraging data spaces technologies, companies can efficiently monitor and adapt to regulatory requirements by analyzing real-time data and responding promptly to changes.
By leveraging data spaces technologies, companies can efficiently monitor and adapt to regulatory requirements by analyzing real-time data and responding promptly to changes.
Customer demand management
Optimized and resilient demand and capacity management for chiplet products necessitates end-to-end visibility of the complete supply chain.
With data-driven insights into customer needs and a common standard for dats sharing across industries for demand and capacity management, companies can optimize their production strategies.
With data-driven insights into customer needs and a common standard for dats sharing across industries for demand and capacity management, companies can optimize their production strategies.
Traceability
Ensuring transparency and traceability throughout the supply chain is crucial for maintaining quality and compliance.
Consolidating and aggregating product data into a digital twin enables seamless documentation and tracking of all production steps, thereby enhancing quality and trust in the products.
Consolidating and aggregating product data into a digital twin enables seamless documentation and tracking of all production steps, thereby enhancing quality and trust in the products.
Adaptive process control with the inclusion of consumables
By exchanging information on material properties and their impact on wafer processing, semiconductor manufacturers and material suppliers can adapt production processes to improve yield and better evaluate the impact of materials on the manufacturing process.
SubFab Optimization
Aims to align sub-fab system control with cleanroom operations to enable energy savings and emission reductions through intelligent, decentralized control based on expert knowledge and real-time data.
Capacity sharing between partners amid disruptions
Aims to enable sharing of capacities between different fabrication plants (Fabs), either within the same company or between different semiconductor companies, to enhance reliability and minimize the impact of disruptions by leveraging collective resources and expertise.
Yield Improvement through data-driven external services
Aims to enable adaptive process control via exchange of data between semiconductor companies and AI service providers to help compensate for minor deviation in processes and improve the yield, allowing for more flexibility and potentially reducing the number of scrapped wafers in the manufacturing process of wafer layers.
Life Cycle Assessment (LCA) methodology with the underlying Life Cycle Inventory (LCI)
Development of a Life Cycle Assessment (LCA) methodology with the underlying Life Cycle Inventory (LCI): Data acquisition of environmentally relevant parameters along the vertical and horizontal supply chain in the semiconductor industry.
Optimization of environmental key figures
Optimization of environmental key figures by statistical mathematical and AI methods: Reduction of PCF by optimization of process steps or procedures in semiconductor manufacturing with AI approaches.
Product Carbon Footprint (PCF)
Exchange of environmental key figures in the PCF submodels according to the Catena-X architecture between partners over EDC (Eclipse Data Connector): Data exchange of the PCF submodels between partners along the semiconductor supply chain according to the one-up-one-down principle.
Standards
PCF Management
Exchange of PCF Data