Efficient TCAD Model for the Evolution of Interstitial Clusters, {311} Defects, and Dislocation Loops in Silicon
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0994-F10-01
Efficient TCAD Model for the Evolution of Interstitial Clusters, {311} Defects, and Dislocation Loops in Silicon Nikolas Zographos1, Christoph Zechner1, and Ibrahim Avci2 1 Synopsys Switzerland LLC, Zurich, CH-8050, Switzerland 2 Synopsys, Inc., Mountain View, CA, 94043
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. INTRODUCTION Ion implantation in silicon creates silicon self-interstitials, which enhance the point defect-mediated diffusion of dopants during successive thermal anneals. These interstitials also form immobile agglomerates that undergo an Ostwald ripening process and act as a temporary storage of interstitials. The kinetics of the point-defect cluster formation and dissolution govern the time evolution of the interstitial supersaturation and, thereby, the transient-enhanced diffusion of dopants. Different types of interstitial cluster have been reported [2]: small irregular interstitial clusters, rod-like {311} defects, and disk-like perfect or faulted dislocation loops. Recently, one more defect type has been observed, rod-like {111} defects, which exist as an intermediate state between {311} defects and dislocation loops [3]. A considerable effort has been devoted in the past to understand the physical mechanisms that control the nucleation, growth, and dissolution of such defects. Both continuum and atomistic approaches have been proposed for predictive technology computer-aided design (TCAD) used for the analysis of deep-submicron silicon devices. For industrial applications, good accuracy and high computational speed of process simulations is crucial, but they are usually competing issues. Zechner et al. [1] demonstrated how to reduce the number of differential equations without sacrificing accuracy. However, the proposed model only covers small interstitial clusters and {311} defects. This article presents a physics-based extension of this model to include faulted dislocation loops, as well as calibration and verification on a wide range of experimental data.
THEORY According to Zechner et al. [1], only three equations representing three cluster sizes (I2, I3, I4) [4] of different binding energies are needed to simulate the regime of small interstitial clusters. The time evolution of the {311} defects is described by two equations or “moments” [5], one for the interstitial concentration trapped in {311} defects (C311) and one for the density of {311} defects (D311). The concentration C311 increases through the capture
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