The optimization field for TCAD applications [79] can be categorized into three major tasks. These are inverse modeling [80], calibration of simulator models [81,82] and the tuning of certain process parameters [83].
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
(5.1) |
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.