Development of Reduced Order Models for Semiconductor Production Equipment

High-fidelity models – such as those based on finite element analysis (FEA), computational fluid dynamics (CFD), and computer-aided engineering (CAE) models – can take hours or even days to simulate and are not suitable for all stages of development. For example, an FEA model that is useful for detailed component design will be too slow to include in system-level simulations for verifying your control system or to perform system analyses that require many simulation runs. A high-fidelity model for chemical vapor deposition will be too slow to run in real time in your embedded system. This is where reduced order modeling (ROM) comes to the rescue. ROM is a set of computational techniques that helps you reuse your high-fidelity models to create faster-running, lower-fidelity approximations.

Join this webinar to learn how to create AI-based reduced order models to replace complex, high-fidelity models. Using the Simulink add-on for Reduced Order Modeling, see how you can perform a thorough design of experiments and use the resulting input-output data to train AI models using pre-configured templates of LSTMs, neural ODE, and nonlinear ARX. Learn how to integrate these AI models into your Simulink simulations for control design, hardware-in-the-loop (HIL) testing, or deployment to embedded systems for virtual sensor applications. The session is pre-recorded with a live Q&A session at the end, held by Christoph Stockhammer.