The purpose of this work is to find a calibrated model for the formation and dissolution of silicon selfinterstitial clusters of {113} or {311} defects. A good calibrated model for selfinterstitial clustering is important for accurately simulating the TED (transient enhanced diffusion) of impurities. TED is the fast displacement of impurities in the first thermal step just after implantation and the simulation of its evolution and magnitude is important in the manufacturing processes of submicron devices [99].
The source of the silicon selfinterstitials was shown to be the defects which are rod like clusters of interstitials [30]. Counting the amount of selfinterstitials is a nontrivial task: from transmission electron micrographs the number of interstitials in each defect and thus the total number has to be measured. In [95] one can find measurements giving the number of interstitials as a function of time for annealing at four temperatures ( , , , and ) and , implants. These measurements are shown in detail in Figure 14.1 and provided the basis for this inverse modeling problem. For the computations we used TSUPREM4 [8] and the optimization framework SIESTA [45,139].
We were interested in finding solutions for two different technologies corresponding to different values of several TSUPREM4 variables. In the following we will call these parameter sets the high and the low parameter set (the latter being the TSUPREM4 default values). The parameters and their values are shown in Table 14.1.
Since the rate of formation and dissolution is not yet fully understood, the model used contains several proposed models (e.g., [99]) as special cases [8]. After describing the model and the details of the inverse modeling problem we present the results and the calibrated model.



Clemens Heitzinger 20030508