Efficient TCAD Model for the Evolution of Interstitial Clusters, {311} Defects, and Dislocation Loops in Silicon

Abstract

The simulation of deep-submicron silicon-device manufacturing processes relies on predictive models for extended defect clusters. For submicroscopic interstitial clusters and {311} defects, an efficient and highly accurate model for process simulation has been developed and calibrated recently [1]. This model combines equations for three small interstitial clusters and two moments for {311} defects. In this work, we extend this model to include dislocation loops and to reproduce a greatly increased range of experimental data, including thermal annealing of end-of-range defects after amorphizing implants.

Evolution of end-of-range defects in silicon-on-insulator substrates

“…We have also preferred to group Perfect Dislocation Loops, faulted dislocation loops and rod-like {1 1 1}s together and to indicate them with the general term "Loops" in all the following plots. The defects belonging to this group are in fact similar in terms of thermal evolution and are normally associated to a single type of defect in many numerical defect models [11]. The obtained results are reported in Figs.…”

On a computationally efficient approach to boron-interstitial clustering

“…In particular, all boron atoms were assumed to be initially on substitutional sites and for each of these boron atoms an extra self-interstitial was introduced. The evolution of these self-interstitials into selfinterstitial clusters and further into extended defects was taken into considerations via the model of Zographos et al [11].…”

From point defects to dislocation loops: A comprehensive TCAD model for self-interstitial defects in silicon

“…A considerable effort, using both continuum [4], [7]- [13] and atomistic [14]- [16] approaches, has been devoted to the understanding of the physical mechanisms that control the nucleation, growth and dissolution of such defects. The continuum method however, is limited by the number of equations that can be solved without running into prohibitive CPU demands and/or convergence instabilities.…”