Simulation of Doping Processes

Ryssel, H.; Haberger, K.; Hoffmann, K.; Prinke, G.; Dumcke, R.; Sachs, A.

doi:10.1109/jssc.1980.1051437

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…The Pearson VII distribution has very limited applicability, not being skewed [16]. The Pearson IV function is most favoured for the modeling of implantation profiles, and it has been reported that almost all practically arising profiles can be fitted rather accurately by it [24], [25]. Under the restriction the solution of the differential equation (1) is the Pearson IV function, which can be written as…”

Section: Analytical Modelsmentioning

confidence: 99%

Analytical Models for Three-Dimensional Ion Implantation Profiles in FinFETs

Wang

Brown

Cheng

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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A set of analytical models based on the Pearson distribution function applied to the modeling of ion implantations in the 3-D structure of FinFETs is presented. The method provides a succinct way to simulate the doping profiles in FinFETs at low computational cost. Compared to previous analytical methods based on Gaussian distributions, this approach handles more realistic asymmetrical doping distributions arising from ion implantation. A simulation module in C + + has been developed to model ion implantations in FinFETs based on this analytical approach. The simulation module is demonstrated in an example simulation of a silicon-on-insulator FinFET with physical gate length of 20 nm.

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Section: Analytical Modelsmentioning

confidence: 99%

Analytical Models for Three-Dimensional Ion Implantation Profiles in FinFETs

Wang

Brown

Cheng

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Their distribution in the surface layer of Si varies only slightly with the segregation coefficient even at m s > 10. Therefore, m s = 100 was taken for P and Ge [22]. The constant k b of SI recombination on background bulk traps is determined by the SI diffusion length L I k b = D I L 2 I , which depends on the concentration of residual impurities and defects in the Si bulk and is approximately ∼1 µm [23].…”

Section: General Statements and Equations Of The Modelmentioning

confidence: 99%

Write Laser Power Normalization Method for Magneto-Optical Disk Drives

Hara¹,

Ohishi

Kubo

1996

Jpn. J. Appl. Phys.

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The diffusion-segregation redistribution of B and P impurities during thermal oxidation of Ge-containing Si has been simulated. In all probability, Ge affects the diffusion of B and P via bulk recombination of self-interstitials (SI) on Ge centres, rather than through a decrease in the rate of surface generation of SI in thermal oxidation. The Ge-induced retardation of the oxidation-enhanced diffusion of B and P in Si and redistribution of the B and P impurities at the SiO 2 /Si interface are described. The parameters of SI bulk recombination on Ge-related centres are found and an increase in the segregation coefficient of B in the SiO 2 -Si system in the presence of Ge is revealed by comparing the calculated and experimental concentration distributions.

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“…The pre- dictions of the Furukawa theory, the Winterbon code using a Pearson distribution vertically multiplied by a Gaussian distribution laterally (following [45]), and the TRIM code are shown. From this figure it can be seen that the lateral spreading, as well as the spreading in the vertical direction, as calculated by the TRIM code, varies drastically from that which would be expected on the basis of a Gaussian distribution, with the maximum lateral penetration occurring at the projected range (~50 nm) for the assumption of uniform lateral scattering, while the TRIM code predicts maximum lateral penetration at roughly three times the projected range (~150 nm).…”

Section: Comparison Of Predictionsmentioning

confidence: 99%

“…The alternative approach, adopted by Ryssel et at. [45] is to multiply the vertical distribution by a Gaussian (for reasons that are not readily apparent). When Monte-Carlo simulation techniques are applied to a grid with depth resolution element A, eg (3.14) becomes G(x ,y,z) =^^F (x,y-nA, z-i•mA) , (3.15) n,m where the indices n and m are chosen to fill the area A.…”

Section: Comparison Of Theoriesmentioning

confidence: 99%