An electric stopping power model for Monte Carlo and molecular dynamics simulation of ion implantation into silicon
Author(s) -
Dan Cai,
Niels GrønbechJensen,
C.M. Snell,
Keith Beardmore,
Stephen Morris,
A.F. Tasch
Publication year - 1996
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/276925
Subject(s) - stopping power , atomic physics , molecular dynamics , ion , monte carlo method , electron , dopant , radius , silicon , materials science , physics , molecular physics , computational physics , nuclear physics , doping , condensed matter physics , optoelectronics , quantum mechanics , statistics , mathematics , computer security , computer science
We develop a phenomenological model of electronic stopping power for modeling the physics of ion implantation into crystalline silicon. In the framework of effective charge theory, this electronic stopping power for an ion is factorized into (1) a globally averaged effective charge taking into account effects of close and distant collisions by target electrons with the ion, and (2) a local charge density dependent electronic stopping power for a proton. This model is implemented into both molecular dynamics and Monte Carlo simulations. There is only one free parameter in the model, namely, the one electron radius r{sup degrees}{sub r} for unbound electrons. By fine tuning this parameter, it is shown that the model can work successfully for both boron and arsenic implants. We report that the results of the dopant profile simulation for both species are in excellent agreement with the experimental profiles measured by secondary-ion mass spectroscopy (SIMS) over a wide range of energies and with different incident directions. This model also provides a good physically-based damping mechanism for molecular dynamics simulations in the electronic stopping power regime, as evidenced by the striking agreement of dopant profiles calculated in the molecular dynamics simulations with the SIMS data
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