The Victory Process conformal export is generated from the volume planes specified in a user deck. The accuracy and resolution of the export is currently controlled by these planes. Local refinement gives a further level of user control that allows the mesh density to be increased near regions of interest. We now support: interface, junction, box and global refinement schemes. These refinement schemes behave similarly to the Delaunay export, with the exception that the distance parameter is calculated automatically. This is necessary since the conformal nature of the mesh would be lost if the wrong distance is used.

Device simulation helps users understand and depict the physical processes in a device and to make reliable predictions of the behavior of the next device generation. Device simulations with properly selected, calibrated models and an appropriate mesh structure are very useful for predictive analysis of novel device structures. This helps provide improved reliability and scalability, while also helping to increase development speed and reduce risks and uncertainties.

There is a constant demand to lower the on-state resistance of devices, improving their energy efficiency as well as increasing their current handling capabilities whilst maintaining the desired breakdown voltage. However, a trade-off relationship exists between the on-state resistance and breakdown voltage. This is referred to as the silicon limit [1]. Attempts have been made to break the silicon limit. One such method proposed by several authors is the Vertical LOCOS MOS (VLOCOS) [2,3].

VWF allows a curve as target for optimization. This feature greatly benefits the users who need to calibrate TCAD simulation against measured curve such as SIMS. VWF offers multiple ways to use experimental curve as the target of optimization. In this article, two ways of defining curve target are discussed, 1) using vector target; 2) using target definition language of Dbinternal. Which method to choose depends on the optimizer that is being used to solve the problem. Method (1) is suitable for Levenberg-Marquardt, whereas method (2) needs to be used if any of the global optimizers (e.g. genetic optimization) is being selected.