In recent years, ion implantation has become one of the key techniques in semiconductor fabrication. The annealing of the damage produced during implantation is, however, not fully understood. Ion implantation at high temperatures allows the time-resolved study of implantation-enhanced diffusion. During the process, point defects are generated by the ion implantation and consumed by recombination in the bulk as well as by diffusion to the surface and recombination there. With increasing temperatures, the recombination of point defects, which are acting as diffusion vehicles, results in reduced effective diffusion. Profiles processed above 900 show marked uphill diffusion at the surface caused by large gradients of the point defect concentrations. This uphill diffusion affirms the generally accepted pair diffusion theories. Since the point defects are in steady state even after process times which are short compared to the total process time, we are able to give a qualitative analysis of the dose dependence of the diffusion. By extensive numerical simulations, we could estimate the product of bulk recombination rate and equilibrium concentrations of self-interstitials and vacancies as well as the interface recombination velocity for the self-interstitials. The results obtained are in qualitative agreement with previous work of others. The results demonstrate, in fact, clearly the advantages of the method presented. But due to experimental problems concerning the temperature measurement, which have not been fully resolved up to now, the results have to be considered as crude estimates.

The high accuracy which is necessary for modern process simulation often requires the use of Monte-Carlo ion implantation simulation methods with the disadvantage of very long simulation times especially for three-dimensional applications. In this work a new method for an accurate and CPU time efficient three-dimensional simulation of ion implantation is suggested. The approach is based on a combination of the algorithmic capabilities of a fast analytical and the Monte-Carlo simulation method.