Modelling and Simulating the Selective Epitaxial Growth of ... - Imec

Modelling and Simulating the
Selective Epitaxial Growth of Silicon under
Consideration of Anisotropic Growth Rates
Rainer G. Spallek*, Dietmar Temmler , Thomas Preußer*, Torsten Rönsch*, Stefan Ulbrich*
*Dresden University of Technology, Department of Computer Engineering, D-01062 Dresden
¡ Infineon Technologies Dresden, D-01099 Dresden
Abstract
¢ rgs,preusser,roensch,ulbrich£ @ite.inf.tu-dresden.de, dietmar.temmler@infineon.com
This work presents a new model for the simulation of
thin layer deposition and etching processes. Especially
suited for the simulation of highly anisotropic processes,
the frontier model is introduced in the context of selective
epitaxial growth of silicon. The general approach to this
new simulation model is described, and the simulation of
selective epitaxial growth is validated against experimental
results.
1. Introduction
Epitaxial growth of silicon is widely used to decrease
the defect density in the wafer production [1]. It has also
been applied to the fabrication of bipolar transistors [2], to
the advanced BiCMOS technology [3] and to the production
of high voltage circuits [4].
The advancing compactification of semiconductor devices
entailed different attempts in DRAM technology to
exploit the third dimension, such as vertical transistors
[5] and epi-buried-straps [6]. The latter concept realizes
a stacked DRAM design by implementing the selection
transistor of a DRAM cell partly on epitaxially grown silicon,
thus moving it closer to or even partially above the
deep trench capacitor.
Today’s standard process simulation software does not
include models for selective epitaxy. Heeding the special
nature of silicon epitaxy, this paper introduces the frontier
model as a new concept for the simulation of thin
layer deposition and removal. Section 2 outlines the physical
background of the epitaxy process; section 3 introduces
the concept of the new simulation model; section 4
presents the obtained results and their experimental validation;
and section 5 concludes the paper.
2. Physical background
The epitaxial growth of silicon is regularly performed
as a Chemical Vapor Deposition (CVD) process deploying
silane (¤¦¥¨§�© ) or one of its chlorine derivates (¤¦¥�§�©���������� )
as gaseous silicon source often accompanied by hydrogen
(§�� ) and hydrogen chloride (§���� ). A dichloro silane
¤¦¥�§ � ��� � (g)
¤�¥���� � (*) ��§ � (g)
��� (gas)
� (g) ��§ � (g)
¤¦¥����
��� (surface)
(s) ����§���� (g)
¤¦¥
g — gaseous; s — solid; * — activated
Figure 1: Dichloro silane process
Figure 2: Growth of a ¢������
£ -face
) process in an § � /§���� -enriched atmosphere (����¤ 1 provided
the reference for the calibration and validation of
our simulation model. This process was thoroughly studied
in [7] where dichloro (¤¦¥������ silylene ) was identified
as the main species responsible for the surface absorption
through reactions similar to those shown in Figure 1.
During selective epitaxial growth (SEG), deposited silicon
atoms tend, by surface diffusion, to fill holes in the
surface plane to reduce the number of dangling bounds.
Figure 2 illustrates this effect for a ¢������
-face, where
£
only atoms within the surface layer are left with two open
bounds. The same holds for the ¢������
-face. Other faces
£
frequently observed after SEG processes are ¢������
,
¢������
£
and, occasionally, £ ¢������
are assumed to be composed of different crystallographic
steps [8].
Process parameters of the SEG range from temperature,
total and partial gas pressures to gas flow properties
and the current surface structure. An increase of the process
temperature or the total pressure speeds up the growth
£ , the last two of which
1 performed on a 200 mm-Epi-Centura by Applied Materials

{110}
{113}
{001}
Nitride Nitride
Silicon
{113}
Figure 3: Schematic SEG process
{110}
and results in softer contours, i. e. less clearly distinguishable
faces. Higher pressure also reduces the selectivity of
the growth, a property majorly influenced by the share of
§���� in the total gas flow. Low §���� concentrations enable
the nucleation of silicon on all surfaces whereas high §����
concentrations yield a selective growth exclusively on silicon
surfaces or even a net etching of silicon.
While selectivity is not an issue during substrate preparation,
it is a valuable instrument during device fabrication.
Since SEG directly affects the device properties,
its simulation is critical. On monocrystal silicon, extremely
anisotropic growth rates dependent on the crystallographic
faces are observed, which establish a challenge
for the simulation tool. Figure 3 depicts the typical growth
schematically.
{110}
{113}
{001}
{113}
{111} {111}
{110}
Figure 4: Growth rate as a function of growth direction
3. Simulation model
Typical process simulators describe the device structure
by a set of discretisation points. Since SEG simulation
essentially requires the movement of the surface, a
model assigning a displacement to each surface point � is
applied:
�����������������
1
Silicon
2
4
3
Nitride
Figure 5: Basic frontier growth in frontier model
where the growth rate ������� can depend on the material
at � (selectivity), surface direction (anisotropic growth),
process temperature and pressure and on the simulation
time step.
Our implementation of the SEG simulation was incorporated
into DUPSIM, a 2D device and process simulator
developed at Dresden University of Technology. For the
processed device structures, DUPSIM uses a string representation
describing layer boundaries as polylines. Although
initial simulations were performed with rate models
like the one shown in Figure 4, manually fitted to specific
process parameters 2 , no satisfying results were obtained.
The string model turned out to be incapable of
handling the extremely anisotropic growth rates and generating
correct faces. These deficiencies of the string approach
are due to the concave shape of the applied rate
model, which leaves the exact behavior of a surface movement
undefined in angles.
We introduce the frontier model as a new approach to
model the growth of crystallographic faces. The central
idea is to move the line segments defining the surface instead
of its discretization points. Each segment is translated
perpendicularly according to the rate obtained from
the rate model. Wherever two line segments meet at an
angle, certain intermediate directions are introduced to allow
the formation of new faces. These directions can be
generated within the simulation software or taken from a
crystallographic knowledge base of preferred growth directions.
Finally, the new surface is determined by concatenating
the intersection points of the possibly extrapolated
line segments.
The basic process of frontier growth is illustrated in
Figure 5. The growth is performed selectively only on
silicon surfaces. Also note the additional frontiers at the
corner of the silicon structure, not all of them persisting
within the final polyline of the new surface.
2 850 � C, 15 Torr, 32 slm ��� , 0.26 slm ����� , and 0.16 slm ����� in a
��������� cross section

Nitride
Silicon
Figure 6: Overgrowth at material boundaries
Similarly to the loop elimination in string model implementations,
the frontier model needs to be able to identify
frontiers that are relevant to a proper surface description.
Assume the surface described in Figure 5 is processed
from left to right. Line segment 1 be given in parametric
form by its leftmost point and a horizontal direction
pointing to the right. Each succeeding line segment is defined
by its intersection � point with its predecessor, and
a �
�
vector in processing direction:
�����
���������
�
A line segment whose presumed successor intersects at a
point with ��� 0 is irrelevant. This holds for segment 3 of
Figure 5. Thus, the identification of irrelevant line segments
can easily be done during the transformation and
does not require an additional processing step.
Although other materials are overgrown by epitaxial
silicon, a material boundary establishes a discontinuity enabling
the formation of new faces. Our implementation
assumes the boundary point to be the seed of the epitaxial
overgrowth. Therefore, as illustrated in Figure 6, all faces
throughout a 270 ¡
loop are calculated speculatively. The
first intersection point of the surface of the neighboring
material with this loop then determines the first relevant
segment.
4. Experimental validation
The primary test structure for the calibration and validation
of our simulation was a silicon window. A layer of
80 nm of silicon nitride was deposited on 20 mm wafers
with an initial growth of padoxide. Following lithography
and etching steps produced windows of about 140 nm diameter
within the nitride layer.
Various SEG processes were conducted on wafers with
those structures. Our experiments spanned a temperature
range from 850 to 1000 ¡ C and were carried out with an
overall pressure of 15 Torr and varying partial gas pressures.
The standard experiment the results refer to was
a 850 ¡ C process at 15 Torr with gas flows of 32 §�� slm ,
0.26 slm DCS and 0.16 §���� slm .
Measurements were performed within SEM (scanning
electron microscope) pictures, as the one presented in Figure
7 showing a ¢������
cross section. The typical stable
£
Figure 7: Silicon window – experimental result
Figure 8: Silicon window – simulation result
crystallographic faces [8] at ¢������
, £ ¢������
and £ ¢������
£
can be observed.
Experiments with different target thicknesses from 30
to 170 nm provided the data of the epitaxial growth of the
main faces, which was used to calibrate the rate model in
the crystallographic knowledge base of preferred growth
directions within the process simulator DUPSIM. Figure 8
shows a typical simulation result, including a final reflow
process step.
To show that, beyond the qualitative congruence, there
is also a quantitative match of experimental and simulation
results, various measurements have been done. The
definitions of the measurement points are given in Figure
9. They reflect the most important features of the
grown structure. A direct comparison of experimental and
simulation results, as provided in Table 1, shows a close
correspondence between the physical process and its simulation.
5. Conclusion
The frontier model is introduced as a new approach
to model the growth of crystallographic faces. By moving
whole frontiers instead of just discretization points (as
in the string model), extremely anisotropic growth rates,
which occur in the selective epitaxial growth of silicon,
can be handled.
The implementation of the frontier model strongly
benefits from the close contact to the practical epitaxy experiments.
So the rate model is directly derivated from
the measurement results of real selective epitaxial growth
processes.
Experimental validation of the results shows a good
congruence between simulation and the physical process
of selective epitaxy. Moreover, the advantages of the new
concept may also be used for etching and other processes
of thin layer deposition and removal.

4a 3 4b
1
Figure 9: Silicon window – measurement template
experimental
results [nm]
simulation
results [nm]
target thickness [nm]
30,0 90,0 130,0 170,0
1 173,0 165,1 169,0 171,0
2 31,5 95,3 136,6 174,9
3 141,5 45,2 26,5 17,7
4a 23,6 63,9 69,8 80,6
4b 20,6 64,9 83,5 82,6
1 175,0 175,0 175,0 175,0
2 30,5 93,5 136,0 178,0
3 128,9 55,3 32,2 6,7
4a 23,0 59,9 71,4 84,2
4b 23,0 59,9 71,4 84,2
Table 1: Comparison: experimental vs. simulation results
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