12 Surface diffusion on non-metallic solids

Allen, C. E.; Seebauer, Edmund G.

doi:10.1007/10542761_5

Landolt-Börnstein - Group III Condensed Matter

DOI: 10.1007/10542761_5

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12 Surface diffusion on non-metallic solids

C. E. Allen¹,

Edmund G. Seebauer²

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Cited by 2 publications

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“…The simulations show that the final diffusion profile cannot be fitted using a single effective coefficient of diffusion in the model. Thus, the B diffusion in the poly-Si layer is neither of type A nor of type C at 650 °C [10]. In consequence, the model of Fisher [11] has been used in order to simulate a diffusion of type B.…”

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confidence: 99%

Dopant Diffusion during Amorphous Silicon Crystallization

Portavoce

Simola

Mangelinck

et al. 2007

DDF

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We have investigated the redistribution of B during the crystallization of an amorphous Si layer homogeneously doped with P. The redistribution of B only occurs for concentrations lower than 2 × 1020 at cm−3. Crystallization leads to a non “Fickian” redistribution, allowing an abrupt interface between the regions doped and undoped with B. Once the crystallization is ended, B diffuses through the layer in the type B regime with a coefficient which is in agreement with the literature data for diffusion in polycrystalline Si. Although the P distribution is homogeneous in the entire layer, for a temperature as high as 755 °C, P diffuses towards the region the most concentrated in B. The B and P interactions are interpreted as chemical interactions.

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confidence: 99%

Dopant Diffusion during Amorphous Silicon Crystallization

Portavoce

Simola

Mangelinck

et al. 2007

DDF

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Artificial Neural Network Based Process Model for Arsenic Diffusion in Si1-xGex Alloys

Sharma

Mane

2011

AMR

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The thermal diffusion behavior of ion-implanted Arsenic (As) in SiGe alloy has been investigated and modeled. This paper introduces a neural network based model consisting of physics-based and process-based parameters for evaluating the effective diffusivity of Arsenic through SiGe accurately. The parameters that served as the input to the neural network included Ge content, diffusion temperature and anneal time. The model was validated for the germanium content of up to 45% with the reported data and the existing simulation models in Silvaco. The model incorporates all the effects associated with the change in the process parameters which affect the diffusivity of As in relaxed-SiGe. The model was found to be extremely accurate in predicting the exact dependencies of As diffusivity on physics-based and process parameters. The proposed empirical process model may find suitable application in prediction of thermal diffusion behavior of As in SiGe process-flow with emphasis on reduced computational time.

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12 Surface diffusion on non-metallic solids

Cited by 2 publications

References 74 publications

Dopant Diffusion during Amorphous Silicon Crystallization

Dopant Diffusion during Amorphous Silicon Crystallization

Artificial Neural Network Based Process Model for Arsenic Diffusion in Si1-xGex Alloys

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