Automatic Parameter Tuning in Finite Element Analysis of Semiconductor Packages

The solvers of the simulators with underlying stiff equations usually come with a large number of parameters that need to be tuned, also known as configuration settings. The developers of such simulators provide, in most of the cases, a default set of values for such parameters which are adequate for large number of problems. However, there are cases where the convergence is not reached using the default parameters values. Often in these cases, the simulation time becomes very high and a manual stop is required. The complex correlation between parameters and their high impact on the algorithm convergence implies that manual fine tuning is very time consuming. The default parameters set is usually far from optimal with regard to simulation time. Therefore, for an application requiring a large number of simulations, it will be convenient to have a tool that minimizes the simulation time, while also maintaining the accuracy. This paper presents an analysis tool that can be used to speed-up the parameter tuning process by up to 10 times. This tool relies on Machine Learning algorithms and assures the convergence of the solver through active learning and adaptive verification.