Application of selective epitaxy for formation of ultra shallow SiGe-based junctions

Hållstedt, Julius; Isheden, Christian; Östling, Mikael; Baubinas, R.; Matukas, Jonas; Palenskis, Vilius; Radamson, Henry H.

doi:10.1016/j.mseb.2004.07.052

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Whatever the Ge content of the layer, a significant increase of the growth rate occurs as diborane is sent in larger and larger amounts on the growing surface, in agreement with Refs. [16,24] findings for B-doped Si and Si 0.8 Ge 0.2 . Growth rates increases have also been obtained when depositing at 575 1C boron-doped Si 0.77 Ge 0.23 and Si 0.72 Ge 0.28 layers with a silane+germane+diborane chemistry [25] (instead of a heavily chlorinated chemistry as in Refs.…”

Section: Layer Resistivity; Impact Of Boron Doping On the Sige Growthmentioning

confidence: 99%

“…Growth rates increases have also been obtained when depositing at 575 1C boron-doped Si 0.77 Ge 0.23 and Si 0.72 Ge 0.28 layers with a silane+germane+diborane chemistry [25] (instead of a heavily chlorinated chemistry as in Refs. [16,24]). This is explained in terms of enhanced H desorption when B atoms are present on the surface (freeing surface sites for the adsorption of Si or Ge gaseous precursors).…”

Section: Layer Resistivity; Impact Of Boron Doping On the Sige Growthmentioning

confidence: 99%

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Growth kinetics and boron doping of very high Ge content SiGe for source/drain engineering

Hartmann

Gonzatti

Fillot

et al. 2008

Journal of Crystal Growth

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Section: Layer Resistivity; Impact Of Boron Doping On the Sige Growthmentioning

confidence: 99%

Section: Layer Resistivity; Impact Of Boron Doping On the Sige Growthmentioning

confidence: 99%

Growth kinetics and boron doping of very high Ge content SiGe for source/drain engineering

Hartmann

Gonzatti

Fillot

et al. 2008

Journal of Crystal Growth

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“…[13][14][15] This point is highlighted when SEG faces pattern dependency in which the SiGe layer profile is affected by the pattern layout. [16][17][18][19][20][21][22][23][24][25][26] During recent years, various methods have been proposed to decrease the pattern dependency in SEG of SiGe layers but an effective method that completely eliminates this problem has not yet been presented. 23 Bodnar et al 16 proposed that the pattern dependency can be reduced remarkably by applying a high HCl partial pressure.…”

mentioning

confidence: 99%

Kinetic Model of SiGe Selective Epitaxial Growth using RPCVD Technique

Kolahdouz

Maresca

Ghandi

et al. 2011

J. Electrochem. Soc.

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Recently, selective epitaxial growth (SEG) of B-doped SiGe layers has been used in recessed source/drain (S/D) of pMOSFETs. The uniaxial induced strain enhances the carrier mobility in the channel. In this work, a detailed model for SEG of SiGe has been developed to predict the growth rate and Ge content of layers in dichlorosilane(DCS)-based epitaxy using a reduced-pressure CVD reactor. The model considers each gas precursor contributions from the gas-phase and the surface. The gas flow and temperature distribution were simulated in the CVD reactor and the results were exerted as input parameters for Maxwell energy distribution. The diffusion of molecules from the gas boundaries was calculated by Fick's law and Langmuir isotherm theory (in non-equilibrium case) was applied to analyze the surface. The pattern dependency of the selective growth was also modeled through an interaction theory between different subdivisions of the chips. Overall, a good agreement between the kinetic model and the experimental data were obtained.

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“…4 have to be minimized. The importance to establish an epi-model for SEG is highlighted when the growth faces pattern dependency in which the SiGe layer profile is affected by the pattern layout [15,17,21,38,[40][41][42][43][44]. During recent years, various methods have been proposed to decrease the pattern dependency in SEG of SiGe layers but an effective method which completely eliminates this problem has not yet been presented [15,17].…”

Section: Pattern Dependency Of Segmentioning

confidence: 99%