Silvaco Group, Inc. announced its partnership with Dassault Systèmes to develop interoperable digital twin workflows for semiconductor manufacturing. The collaboration brings together complementary simulation technologies spanning equipment-scale plasma simulation, feature-scale semiconductor process modeling, and structural analysis. By connecting these domains through integrated workflows, the companies aim to help semiconductor manufacturers better understand how equipment conditions influence wafer-level outcomes, enabling more informed engineering decisions and accelerating progress toward first-time-right process development.
The Silvaco Group, Inc. and Dassault Systèmes partnership is intended to help customers build a more connected digital representation of semiconductor manufacturing processes, giving engineering teams broader visibility into how upstream manufacturing decisions can influence downstream process and structural outcomes. By improving interoperability between complementary simulation technologies, customers can investigate interactions across multiple scales, identify potential issues earlier in development, and make decisions with greater confidence. The companies intend to connect plasma simulation capabilities within Dassault Systèmes?
SIMULIA applications with Silvaco Victory Process feature-scale semiconductor process simulation. This workflow is designed to support multiscale studies of advanced plasma-based etch and deposition processes and help customers better understand the relationship between equipment conditions and wafer-level feature evolution. The companies plan to enable transfer of structures and material information generated within Silvaco process simulation workflows into Dassault Systèmes?
SIMULIA structural analysis environments. This capability is intended to support stress, deformation, and related structural analyses that are increasingly important for advanced semiconductor technologies. The partnership will focus on developing interfaces and workflow definitions that allow engineering data to move efficiently between complementary simulation technologies while preserving the information required by downstream analyses.
These connected workflows are expected to provide an important foundation for broader semiconductor manufacturing digital twin initiatives. The new workflows are intended to provide customers with several important advantages: Enable more informed engineering decisions through a clearer understanding of relationships between equipment conditions, process behavior, and structural outcomes. Support first-time-right manufacturing objectives by allowing engineers to evaluate more alternatives virtually before expensive experimentation in the fab.
Reduce manual handoffs and repeated reconstruction of geometry, material, and process information across simulation environments. Improve traceability between upstream process inputs and downstream manufacturing outcomes. Accelerate learning cycles and process optimization through more connected multiphysics analysis.
Establish a stronger digital foundation for advanced manufacturing digital twin deployments. The initial focus of the partnership is practical interoperability between complementary simulation technologies. Over time, the companies believe these connected workflows can help advance first-time-right semiconductor manufacturing by enabling more predictive, data-driven engineering decisions earlier in the development cycle.
By improving the flow of information across simulation domains, customers can evaluate more alternatives virtually, identify issues sooner, reduce development risk, and accelerate progress toward high-volume production. By linking equipment-scale physics, feature-scale process simulation, and structural analysis within connected workflows, customers can create more comprehensive digital representations of manufacturing processes to support engineering analysis, optimization, and operational decision-making. These digital twin capabilities are intended to help engineering teams better understand cause-and-effect relationships across manufacturing stages, accelerate yield ramps, and bring new semiconductor technologies to production with greater confidence.