The optimization field for TCAD applications  can be categorized into three major tasks. These are inverse modeling , calibration of simulator models [81,82] and the tuning of certain process parameters .
A TCAD optimization task is an iterative minimization or maximization process, where the optimizer controls a set of free parameters of a model within certain upper and lower bounds to minimize or maximize a given target. A simulator that takes the model as input is used to compute an error vector. The target is then computed as the quadratic mean of the dimensional error vector
Several iterations are performed until a truncation criterion is reached. For the case of a local optimizer the criterion is usually a minimum change of the target value. For a global optimizer additional criteria like, e.g. a maximum iteration number might be defined. An optimization framework integrates optimizer and simulator and spreads the simulation jobs over a cluster of workstations. Fig. 5.1 depicts this scenario. Several concrete sub-problems of this abstract optimization task exists which are briefly sketched in the following sections.