Modeling of electrical activation ratios of phosphorus and nitrogen doped silicon carbide | IEEE Conference Publication | IEEE Xplore
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Modeling of electrical activation ratios of phosphorus and nitrogen doped silicon carbide


Abstract:

We propose an empirical model to accurately predict electrical activation ratios of phosphorus and nitrogen implanted silicon carbide for arbitrary annealing temperatures...Show More

Abstract:

We propose an empirical model to accurately predict electrical activation ratios of phosphorus and nitrogen implanted silicon carbide for arbitrary annealing temperatures. We introduce model parameters and compare the activation behaviour of the two donor-type dopants. Our investigations show that the activation ratio of the nitrogen implanted silicon carbide is similar to a step function, while the activation ratio for the phosphorus implanted silicon carbide increases continuously with post-implantation annealing temperature. The model has been implemented into Silvaco's Victory Process simulator, which has enabled accurate predictions of dopant activation in postimplantation steps for silicon carbide-based processes. Several simulations have been performed to extract the depth profiles of the active dopant concentrations and to predict the activation as a function of total doping concentration and annealing temperature.
Date of Conference: 07-09 September 2017
Date Added to IEEE Xplore: 26 October 2017
ISBN Information:

ISSN Information:

Conference Location: Kamakura, Japan

I. Introduction

Semiconductor devices based on silicon carbide (SiC) are typically doped by ion implantation processes followed by thermal annealing steps. This is performed in order to repair the lattice damage caused by the implantation bombardment [1] and to electrically activate the implanted impurities [2]. The post-implantation steps are therefore key to a successful device fabrication and cannot be avoided [3]. However, in order to reach the full potential of electronic devices and at the same time minimize production and operation costs, the accuracy of the process simulations is essential [4].

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References

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