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23 \title{Monte Carlo simulation study\\of a selforganization process\\leading to
24 ordered precipitate structures}
26 \author[augsburg]{F. Zirkelbach\corauthref{cor}}
27 \author[augsburg]{M. Häberlen}
28 \author[augsburg]{J. K. N. Lindner}
29 \author[augsburg]{B. Stritzker}
31 \corauth[cor]{Corresponding author.\\
32 Tel.: +49-821-5983008; fax: +49-821-5983425.\\
33 E-mail address: frank.zirkelbach@physik.uni-augsburg.de (Frank
36 \address[augsburg]{Universität Augsburg, Institut für Physik,
37 Universitätsstrasse 1,\\D-86135 Augsburg, Germany}
40 Periodically arranged, self-organized, nanometric, amorphous precipitates have been observed after high-fluence ion implantations into solids for a number of ion/target combinations at certain implantation conditions.
41 A model describing the ordering process based on compressive stress exerted by the amorphous inclusions as a result of the density change upon amorphization is introduced.
42 A Monte Carlo simulation code, which focuses on high fluence carbon implantation into silicon, is able to reproduce experimentally observed results.
43 By means of simulation the selforganization process gets traceable and detailed information about the compositional and structural state during the ordering process is obtained.
44 Based on simulation results, a recipe is proposed for producing broad distributions of lamellar ordered structures.
48 Monte Carlo simulation; Self-organization; Precipitation; Amorphization;
49 Nanostructures; Ion irradiation\\
50 \PACS 02.70.Uu; 61.72.Tt; 81.16.Rf
55 \section{Introduction}
57 Precipitates, as a result of high-fluence ion implantation into solids, are usually statistically arranged and have a broad size distribution.
58 However, the formation of ordered, lamellar inclusions has been observed for a number of ion/target combinations at certain implantation conditions \cite{ommen,specht,ishimaru}.
59 An inevitable condition for the material to observe this special self-organized arrangement is a largely reduced density of host atoms in the amorphous phase compared to the crystalline host lattice.
60 As a consequence stress is exerted by the amorphous inclusions which is responsible for the ordering process.
61 A model to describe the process is introduced.
62 The implementation of a simulation code based on that model is discussed.
63 Simulation results are compared to experimental data, focussing on high-fluence carbon implantations into silicon.
64 Finally a guideline for fabrication of broad ditributions of lamellar ordered structures is suggested.
67 High-fluence carbon implantations at temperatures between $150$ and $400 \, ^{\circ} \mathrm{C}$ with an energy of $180 \, keV$ result in an amorphous $SiC_x$ layer along with spherical and lamellar amorphous $SiC_x$ inclusions at the layer interface \cite{lamellar_inclusions}, as can be ssen in Fig. \ref{img:tem}.
68 A model is proposed in \cite{model_joerg}, which is schematically displayed in Fig. \ref{img:model}, showing the evolution into ordered lamellae with increasingamount of implanted carbon.
70 With increasing fluence the silcon is supersaturated of carbon atoms which results in a nucleation of spherical $SiC_x$ precipitates.
71 By the precipitation into the amorphous $SiC_x$ ($a-SiC_x$) phase an enormous interfacial energy \cite{int_eng} required for cubic $SiC$ ($3C-SiC$, $a=0.536 \, nm$) in crystalline silicon ($c-Si$, $a=0.543 \, nm$) due to a $20 \, \%$ lattice mismatch can be saved.
72 Since amorphous silicon ($a-Si$) is not stable against ion beam induced epitaxial recrystallization at temperatures above $130 \, ^{\circ} \mathrm{C}$ \cite{ibic}, the existence of the amorphous precipitates must be due to the accumulation of carbon (carbon induced amorphization), which stabilizes the amorphous phase \cite{ap_stab}.
73 In fact, energy filtered XTEM studies \cite{eftem_maik} revealed the carbon-rich nature of the precipitates.
75 The $Si$ atomic density of $a-SiC$ is about $20$ to $30 \, \%$ lower compared to $3C-SiC$ \cite{si_dens1,si_dens2}.
76 The same is assumed for substoicheometrc $a-SiC_x$ compared to $c-Si$.
77 Therefor the amorphous volumes tend to expand and as a result compressive stress - which is relaxing in the vertical direction since the process occurs near the target surface - is applied on the $Si$ host lattice, represented by black arrows in Fig \ref{img:model}.
78 Volumes between amorphous inclusions will more likely turn into an amorphous state as the stress aggravates the rearrangement of atoms on regular lattice sites (stress enhanced amorphization).
79 In contrast, randomly originated amorphous precipitates (ballistic amorphization) located in a crystalline neighbourhood not containing high amounts of carbon will recrystallize in all probability under the present implantation conditions.
81 Since the solid solubility of carbon in $c-Si$ is essentially zero, once formed, $a-SiC_x$ inclusions serve as diffusional sinks for excess carbon atoms in the $c-Si$ phase, represented by the white arrows in Fig. \ref{img:model}.
82 As a consequence the amorphous volumes accumulate carbon enhancing the selforganization process.
86 For the Monte Carlo simulation the target is devided into cells with a side length of $3 \, nm$.
87 Each cell has a crystalline or amorphous state and stores the local carbon concentration.
88 It is addressed by a position vector $\vec{r} = (k,l,m)$ where $k$, $l$ and $m$ are integers.
89 The simulation starts with a complete crytsalline target and zero carbon inside.
91 The model proposes three mechanisms of amorphization.
92 In the simulation, each of this mechanisms contributes to a local amorphization probability of cell $\vec{r}$.
93 The influence of the mechanisms are controlled by simulation parameters.
94 The local amorphization probability at volume $\vec{r}$ is calculated by
96 p_{c \rightarrow a}(\vec{r}) = p_b + p_c c_C(\vec{r}) + \sum_{\textrm{amorphous neighbours}} \frac{p_s c_C(\vec{r'})}{(r-r')^2} \textrm{ .}
99 The ballistic amorphization is constant and controlled by $p_b$.
100 This choice is justified by analysing {\em TRIM} \cite{trim} collision data that show a mean constant energy loss per collision of an ion.
101 The carbon induced amorphization is proportional to the local amount of carbon $c_C(\vec{r})$ and controlled by the simulation parameter $p_c$.
102 The stress enhanced amorphization is controlled by $p_s$.
103 The forces originating from the amorphous volumes $\vec{r}'$ in the vicinity are assumed to be proportional to the amount of carbon $c_C(\vec{r}')$.
104 The sum is just taken over volumes located in the layer and since the stress amplitude is decreasing with the square of the distance $r-r'$ a cutoff radius is used in the simulation.
105 In case of an amorphous volume, a recrystallization probability is given by
107 p_{a \rightarrow c}(\vec r) = (1 - p_{c \rightarrow a}(\vec r)) \Big(1 - \frac{\sum_{direct \, neighbours} \delta (\vec{r'})}{6} \Big) \, \textrm{,}
110 \delta (\vec r) = \left\{
112 1 & \textrm{volume at position $\vec r$ amorphous} \\
113 0 & \textrm{otherwise} \\
117 which is basically $1$ minus the amorphization probability and a term taking into account the crystalline neighbourhood which is needed for epitaxial recrystallization.
119 The simulation algorithm consists of three parts.
120 The first part is the amorphization/recrystallization step.
121 Random values are computed to specify the volume $\vec{r}$ which is hit by an impinging carbon ion.
122 Two uniformly distributed random numbers $x$ and $y$ are mapped to the coordinates $k$ and $l$.
123 A random number $z$ corresponding to the depth coordinate $m$ is distributed according to the nuclear stopping power gained by {\em TRIM} using the rejection method.
125 Again analysing {\em TRIM} collision data shows the nuclear stopping power being essentially
126 The amorphization or recrystallization probability is computed and another random number decides whether there is amorphization or recrystallization or
131 \section{Summary and conclusion}
133 \begin{thebibliography}{20}
134 \bibitem{ommen} A. H. van Ommen, Nucl. Instr. and Meth. B 39 (1989) 194.
135 \bibitem{specht} E. D. Specht, D. A. Walko, S. J. Zinkle, Nucl. Instr. and Meth. B 84 (2000) 390.
136 \bibitem{ishimaru} M. Ishimaru, R. M. Dickerson, K. E. Sickafus, Nucl. Instr. and Meth. B 166-167 (2000) 390.
137 \bibitem{lamellar_inclusions} J. K. N. Lindner, M. Häberlen, M. Schmidt, W. Attenberger, B. Stritzker, Nucl. Instr. Meth. B 186 (2002) 206.
138 \bibitem{model_joerg} J. K. N. Lindner, Nucl. Instr. Meth. B 178 (2001) 44.
139 \bibitem{int_eng} W. J. Taylor, T. Y. Tan, U. Gösele, Appl. Phys. Lett. 62 (1993) 3336.
140 \bibitem{ibic} J. Linnross, R. G. Elliman, W. L. Brown, J. Matter. Res. 3 (1988) 1208.
141 \bibitem{ap_stab} E. F. Kennedy, L. Csepregi, J. W. Mayer, J. Appl. Phys. 48 (1977) 4241.
142 \bibitem{eftem_maik} M. Häberlen, Bildung und Ausheilverhalten nanometrischer amorpher Einschlüsse in Kohlenstoff-implantierten Silizium, Diploma thesis, Augsburg, 2002 (in Germany).
143 \bibitem{si_dens1} L. L. Horton, J. Bentley, L. Romana, A. Perez, C. J. McHargue, J. C. McCallum, Nucl. Instr. Meth. B 65 (1992) 345.
144 \bibitem{si_dens2} W. Skorupa, V. Heera, Y. Pacaud, H. Weishart, in: F. Priolo, J. K. N. Lindner, A. Nylandsted Larsen, J. M. Poate (Eds.), New Trends in Ion Beam Processing of Materials, Eur. Mater. Res. Soc. Symp. Proc. 65, Part 1, Elsevier,Amsterdam, 1997,p. 114.
145 \bibitem{trim} J. F. Ziegler, J. P. Biersack, U. Littmark, The Stopping and Range of Ions in Solids, Pergamon, New York, 1985.
146 \end{thebibliography}
150 \section*{Figure Captions}
153 \item Cross-sectional transmission electron microscopy (XTEM) image of a $Si(100)$ sample implanted with $180 \, keV$ $C^+$ ions at a fluence of $4.3 \times 10^{17} \, cm^{-2}$ and a substrate temperature of $150 \, ^{\circ} \mathrm{C}$. Lamellar and spherical amorphous inclusions at the interface of the continuous amorphous layer are marked by L and S.
154 \item Schematic explaining the selforganization of amorphous $SiC_x$ precipitates and the evolution into ordered lamellae with increasing fluence (see text).
155 \item Comparison of simulation results and XTEM images ($180 \, keV$ $C^+$ implantation into silicon at $150 \, ^{\circ} mathrm{C}$) for several fluence. Amorphous cells are white. Simulation parameters: $p_b=0.01$, $p_c=0.001$, $p_s=0.0001$, $d_r=0.05$, $d_v=1 \times 10^6$.
156 \item Amorphous cell distribution and corresponding carbon implantation profile. The implantation profile shows the mean amount of carbon in amorphous and crystalline volumes as well as the sum for a fluence of $4.3 \times 10^{17} \, cm^{-2}$.
157 \item Simulation result for a $2 \, MeV$ $C^+$ irradiation into silicon doped with $10 \, at. \%$ carbon by multiple implantation steps between $180$ and $10 \, keV$. $20 \times 10^6$ simulation steps correspond to a fluence of $0.54 \times 10^{17} \, cm^{-2}$.
165 \includegraphics[width=14cm]{k393abild1_e.eps}
174 \includegraphics[width=14cm]{modell_ng_e.eps}
183 \includegraphics[width=14cm]{dosis_entwicklung_all_e.eps}
192 \includegraphics[width=14cm]{ac_cconc_ver2_e.eps}
195 \label{img:carbon_distr}
201 \includegraphics[width=14cm]{multiple_impl_e.eps}
204 \label{img:broad_lam}