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

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

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