This webinar presents a comprehensive methodology for the design and optimization of a 1200V Silicon Carbide (SiC) Double-Diffused Metal-Oxide-Semiconductor Field-Effect Transistor (DMOS FET) utilizing Silvaco's TCAD platform. Adhering to industry best practices, the construction of an accurate digital twin is also presented, ensuring conformity to real-world performance characteristics. Moreover, we outline strategies to streamline simulation workflows, emphasizing techniques to boost design efficiency and productivity.
In parallel, this webinar explores the transformative impact of Machine Learning (ML) on traditional device design practices. By harnessing ML algorithms, a fundamental shift occurs in the approach towards device design, enabling process and device engineers to extract insights from vast datasets, optimize performance, and expedite the design iteration process.
Additionally, the necessity of incorporating compact modeling into the design flow is underscored. Compact modeling serves as a crucial component in capturing device behavior accurately, enabling efficient circuit-level simulations and facilitating rapid prototyping of devices. The integration of compact modeling enhances the design process, facilitating a more holistic understanding of device behavior and performance characteristics.
Through the integration of TCAD, simulation optimization, ML techniques, and compact modeling, this webinar demonstrates a synergistic approach towards device design, fostering innovation, precision, and efficiency.