The Influence of an Adsorbate Layer on Adatom Diffusion and Island Nucleation: Fe on Si(111)-√3 x √3-Au
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- Paredis, K., Smeets, D. & Vantomme, A. Nanoscale Res Lett (2009) 4: 1447. doi:10.1007/s11671-009-9418-3
Using scanning tunneling microscopy, the influence of a thin Au layer on the diffusion of Fe adatoms and the subsequent island nucleation on a Si(111) surface is investigated. The adsorbate induces the structure that increases the surface mobility of subsequently deposited Fe atoms, resulting in the formation well-defined nanoclusters. Surprisingly, the domain walls—inherent to the reconstruction—do not influence the surface diffusion, which demonstrates that the passivation is of much more importance for the self-assembly than the surface corrugation. Using the decoupling of the diffusion and nucleationon the surface and the reactionwith the surface and conventional nucleation theory, the activation energy for surface diffusionE d = 0.61 eV and the critical cluster sizei = 3 are determined, which reveal the microscopic details of the diffusion and nucleation processes.
KeywordsSelf-assembly Surface reconstruction Diffusion Passivation STM Nanostructures Iron silicide
In order to assure the continuing downscaling of electronic components, new growth methods need to be explored as currently used top-down techniques are approaching their physical limits. An emerging alternative for the growth of nanoscale systems is self-assembly: by exploiting the striving toward the minimal energy it enables the formation of nanostructures even down to the atomic level. However, its future implementation requires extensive fundamental research to unravel the complex interactions involved. Self-assembly of nanostructures on a surface is predominantly governed by two physical processes: the diffusion of atoms and the subsequent island nucleation. The combination of these processes eventually determines the final properties of the nanostructured systems, such as size, distribution, phase, electrical and magnetic properties, etc. Considering the key role of the surface in the implied interactions, surface functionalization provides a potential way to influence—and eventually to control—the growth of nanostructures on a surface. Our recent results on noble metal induced surface reconstructions prove that an ultra thin buffer layer and the induced surface structure have a major influence on the final morphological island properties [1–3]. In order to obtain a better understanding of the microscopic details of the self-assembly process, we investigated in detail the effect of the Au-induced superstructure on the subsequent diffusion of adatoms and the nucleation of Fe-Si nanostructures on Si(111). Whereas we previously investigated the morphological properties (e.g., size, height, phase formation, density, etc.) of the islands on different Au-induced surface reconstructions  and the Cu-induced reconstruction , we now specifically focus on the reconstruction to investigate the microscopic details of the diffusion and nucleation processes on this particular surface. As it consists of domains separated by domain walls that may act as non-reactive diffusion barriers, a study of the influence of these domain walls on subsequent nanostructure formation can reveal the relative importance of the surface topography versus the surface passivation. Furthermore, to determine the relevant kinetic parameters for surface diffusion and island nucleation on the superstructure, the results are interpreted in the framework of the conventional nucleation theory. It is elaborated that this theory is applicable for our system and as such, we present a novel approach for interpreting surface diffusion and island nucleation in reactive systems.
In analogy to our previous work , Si(111) samples (FZ, 8–12 Ω cm) were cleaned ex situ in a 2% HF solution and in situ using a two-step silicon-flux method . This procedure results in a clean Si(111) surface that exhibits the well known Si(111)-7 × 7 reconstruction. Subsequent deposition of 0.76–0.96 ML Au (1 ML = 7.83 × 1014 at/cm2) at room temperature followed by a 30 min. annealing at 700°C results in the formation of the Si(111)- reconstruction which is thermally stable up to 700°C and exhibits no dangling bonds . A conventional molecular-beam epitaxy (MBE) set-up with a base pressure of 5 × 10−11 Torr was used to deposit the Si, Au and Fe. The deposition rate was monitored in situ with a quartz crystal microbalance, which was calibrated using Rutherford backscattering spectrometry and X-ray reflectivity and was kept constant at 0.015 ML/s for all Fe depositions. After deposition, the sample cooled down and was investigated at room temperature in vacuo by scanning tunneling microscopy (STM). All substrates used in this work have an unintentional miscut of approximately 0.6° relative to the  direction and consequently exhibit surface steps. Due to step bunching, the terraces have widths ranging from 30 to 150 nm.
Results and Discussion
To demonstrate the effect of the reconstruction on the self-assembly of nanostructures , we have first deposited 0.28 ML Fe at 300°C on the bare Si(111)- 7 × 7 surface as a reference. The resulting surface morphology is presented in Fig. 1a. A closer look at the surface reveals a high density of very small grains, randomly distributed. This growth behavior finds its origin in the high concentration of dangling bonds present on the 7 × 7 surface. As a consequence, the surface is highly reactive, thus strongly limiting the diffusion of deposited Fe atoms on the surface: the Fe atoms will rapidly react with the Si surface atoms upon arrival. Next, deposition of the same amount of Fe onto the superstructure at 300°C results in the formation of well-defined nanostructures, as presented in Fig. 1b. Meanwhile, the structure remains present on the entire surface, as evidenced by the inset of Fig. 1b. The drastic change in growth kinetics is induced by the surface, which exhibits no dangling bonds and therefore, is by far less reactive than the 7 × 7 structure. This lower reactivity delays the reaction with the Si atoms and causes a strong increase in the Fe surface diffusion, resulting in the formation of distinct nanoclusters, as discussed previously . We want to emphasize that, as a result of the Au passivation, we are able to create a silicon surface with a strongly reduced reactivity, which is essential for the correct interpretation of our results below.
On the other hand, the STM observations in Fig. 2 reveal that island nucleation itself has a large impact on the domain wall density. In Fig. 2a a surface is shown prior to Fe deposition with an average domain wall density of 14 ± 1 × 10−2nm−1 while after Fe deposition (0.28 ML at 400°C), the average density has significantly increased to 33 ± 2 × 10−2nm−1 (Fig. 2b). As the domain wall density is directly correlated to the Au coverage on the surface, we can conjecture that the Fe atoms penetrate into the Au layer after nucleation, thereby expelling the Au atoms. These atoms are redistributed over the remaining surface which results in the creation of new domain walls, as previously shown in . In the same reference, we have discussed that the islands consist of an iron silicide as well. Consequently, the Au layer acts as a surfactant which significantly enhances the diffusion, but does not inhibit the reaction between the nucleated Fe nanoclusters and the Si substrate. This reaction between the Fe adatoms and the Si substrate is driven by the large difference in the heat of formation (ΔH = −39.56 kJ mol−1 for Fe–Si compared to −17.5 kJ mol−1 for Au–Si) [8–10]. We want to stress that based on these observations, the Au layer causes a decoupling of the diffusion and nucleation on the surface and the reaction with the surface as Fe diffusion and island nucleation take place before the reaction with the substrate occurs. Naturally, the preservation of the reaction is of major importance for the future growth of binary nanostructures.
Additionally, it is observed that the islands preferentially form at the lower step edge at 600°C, (see Fig. 4c), whereas at 300 and 400°C the dots randomly nucleate on the terraces and the step edges (see Fig. 4a, b). This phenomenon is the result of both the passivating Au layer and the elevated temperature, which allow the Fe atoms to reach the step edges, as typical diffusion lengths at 600°C are of the order of 110 nm, which is considerably larger than the average terrace width observed on this surface (approx. 66 nm). These highly coordinated sites are energetically favorable due to the easy access to Si atoms. This is also represented in the schematic diagram in Fig. 3: a surface step gives rise to a local minimum in the surface potential which traps diffusing Fe atoms. Consequently, the Au interlayer not only allows to control diffusion (i.e. island density and size), but also allows to alter the preferential nucleation site.
In this equation η represents a constant dimensionless number near 0.2 containing the coverage dependence, F is the deposition flux, D 0 is the surface diffusion prefactor, i is the critical nucleus size, and E * = (iE d + E b)/(i + 2) is the effective diffusion barrier, which is a weighted sum of the activation energy for diffusion E d and the critical cluster binding energy E b. The energy parameter E * is experimentally accessible from the slope of vs. 1/kT, whereas the critical nucleus size can be obtained from the 1/T = 0 intercept. In Fig. 4d, the Arrhenius plot of the island density n x is shown together with a fit to the data yielding the energy parameter E * = 0.96 ± 0.05 eV and the critical nucleus size i = 3.1 ± 0.3 (using the known flux 0.015 ML/s and nominal values D 0 = 1014/s and η = 0.2) [11, 12]. This implies that a cluster of four atoms is stable and defines a nanostructure. With the value i = 3, the expression for the effective diffusion barrier becomes eV, with E 3 the binding energy of a three-atom cluster. In order to calculate the activation energy E d, the binding energy of a free Fe3 cluster is used as an estimate for E 3, since the Au-passivated surface can be considered inert. Taking the value E 3 = 2.96 ± 0.20 eV, reported by Lian et al. , we find an activation energy for Fe diffusion on the surface of E d = 0.61 ± 0.12 eV, which is lower than the value published by Wohllebe et al.  for Fe diffusion on a Si(111)-7 × 7 surface, E d = 0.76 ± 0.10 eV, in accordance with our observations and expectations. However, it is important to point out here, to be very careful with the comparison with these literature data since they are determined using a theory developed for a non-reactive surface in a study of a (highly) reactive Si surface. For a fully quantitative comparison, reliable values for Fe diffusion on Si(111)-7 × 7, that are currently unavailable, are essential. Nevertheless, the values for the activation energy of the surface diffusion E d and the critical nucleus size i are particularly important for the surface since they determine the microscopic diffusion and nucleation mechanisms on the passivated surface and allow to predict the island density, size and height for a given temperature and deposition rate, which is a key feature in the controlled growth of nanostructures.
In conclusion, we have shown that an ultrathin Au layer has a drastic influence on the subsequent growth of Fe-based nanostructures on the Si(111) surface. Surprisingly, the surface corrugation induced by the domain walls, inherent to the reconstruction, does not significantly affect the surface diffusion. This demonstrates that the passivation of the surface plays a much larger role in the adatom diffusion than the surface topography, which is of major importance for the understanding of surface diffusion. Using a novel approach by applying conventional nucleation theory to this reactive system, we determined the activation energy for surface diffusion on E d = 0.61 eV and the critical nucleus sizei = 3, exposing the microscopic details of the diffusion and nucleation mechanisms. Moreover, these parameters allow to predict the island density, the island size and the island height for a given deposition temperature and rate, which is a major prerequisite in controlling nanostructure growth.
This work was supported by the Fund for Scientific Research, Flanders (FWO), the Concerted Action of the KULeuven (GOA/2009/006), the Interuniversity Attraction Pole (IAP P6/42) and the Center of Excellence Programme (INPAC EF/05/005).