GMSH PARALLEL TRIAL

Currently, device dimensions have been scaled down to the nanometer regime what requires the use of more complicated and more time-consuming simulation methods. Traditionally, when devices were in the micron scale, the resolution of classical models was the most popular choice to predict device behavior, for instance the drift-diffusion (DD) method. Consequently, Technology Computer Aided Design (TCAD) has become an indispensable tool to both predict future device architectures and optimize the present ones.

GMSH PARALLEL TRIAL

For the current transistor dimensions, the traditional process to develop and optimize devices based on trial and error during the manufacturing stage is unviable. This shift in the industry standard calls for new architecture designs able to predict the performance and reliability of new devices.

However, IRDS predictions point toward a change in the leading architectures for the next technology nodes, with promising candidates such as the GAA NW FET and the GAA nanosheet. Silicon device architectures have been developed rapidly during the last few decades, the FinFET being the current standard for mass production. For a reasonably dense mesh of 150k nodes, a variability study of 300 individual simulations can be now performed with VENDES in approximately 2.5 days instead of an estimated sequential execution of 137 days. Also, a reduction of up to 98% in the computational time has been found comparing the sequential and the 16 process simulation. Using a 12-nm-gate-length gate-all-around nanowire FET (GAA NW FET) as a benchmark device, the new implementation shows an almost perfect agreement in the output data with less than a 2% difference between the cases using 1 and 16 processes. We have implemented it using Message Passing Interface in our in-house built semiconductor simulation framework called VENDES, capable of running in distributed systems that allow for more accurate results in a reasonable time frame. In this context, we present a new approach of Schrödinger equation-based quantum corrections. A good compromise between fast and accurate results is to add corrections to the classical simulation that are able to reproduce the quantum nature of matter.

At the same time, the simulation of fully quantum models can be a cumbersome task in certain studies such as device variability analysis, since a single simulation can take up to weeks to compute and hundreds of device configurations need to be analyzed to obtain statistically significative results. Can you help me using a Gmsh mesh in OpenFOAM? If you can share any (tutorial) model for creating an OpenFOAM model using a Gmsh mesh, it would be also highly appreciated.In the current technology node, purely classical numerical simulators lack the precision needed to obtain valid results. I think the problem comes from gmshToFoam, the OpenFOAM mesh is not the same as Gmsh mesh. I tried to run icoFoam and it solved the model without problems, but the solution is not realistic. I studied the mesh in ParaView using paraFoam command and I noticed that the mesh is not the same and the boundary definitions are not correct.Ĩ. I copied some files for solving the model:Ĭp $FOAM_TUTORIALS/incompressible/icoFoam/elbow/system/fvS* system/Ĭp $FOAM_TUTORIALS/incompressible/icoFoam/elbow/constant/transportProperties constant/ħ. In file meshes/polyMesh/polyMeshFromShapeMesh.C at line 576įound 372 undefined faces in mesh adding to default patch.Ħ. I converted the Gmsh mesh to OpenFoam with this command:Īt this point I noticed a warning message:įrom function polyMesh::polyMesh(. GmshToFoam problem: not the same mesh in Gmsh vs.

Gmsh parallel