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Phenomenological Single-Particle
Modeling of Reactive Transport
in Semiconductor Processing

4.2 Reactive Transport in Atomic Layer
Processing

As introduced in Section 4.1, there exists a wealth of complex chemistry in ALP. In order to develop a model which might be applied to real high AR structures, this complexity must be encapsulated in a simpler phenomenological model, since the underlying quantum chemical reality cannot be modeled at such large scale [153, 154]. To illustrate the construction of phenomenological first-order Langmuir surface kinetics, the H2O step of the ALD of Al2O3 is taken as an example. Figure 4.2 depicts the three reaction pathways considered by such a model: An incoming H2O molecule can either adsorb or reflect upon interacting with the surface, mediated by the coverage-dependent sticking coefficient β(Θ) from Eq. (2.4). In addition, an already adsorbed molecule is allowed to spontaneously desorb given a fixed evaporation flux Γev.

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Figure 4.2: Reaction pathways modeled by first-order Langmuir kinetics of the H2O step of the ALD of Al2O3. Reprinted from Aguinsky et al., arXiv: 2210.00749, (2022) [145], © The Authors, licensed under the CC BY-NC-ND 4.0 License, https://creativecommons.org/licenses/by-nc-nd/4.0/.

Under the additional assumption that the H2O supply is the only limiting factor (i.e., ΘTMA(r)=1), the state of the surface is fully determined by the distribution of the H2O coverage ΘH2O(r) given by Eq. (2.3). That is, the particle involved in the reactive transport is the limiting chemical reactant. The complex chemical behavior is then condensed into two phenomenological parameters: β0 and Γev. The challenge with solving Eq. (2.3) is that it depends on the impinging water flux Γimp which itself depends on the surface state according to β(Θ).

To break this loop, Eq. (2.3) is discretized using the forward Euler method up to the pulse time tp, obtaining

(4.5)ΘH2On+1(r)=ΘH2On(r)+hts0Γimp,H2On(β(ΘH2On),r)β0(1ΘH2On(r))hts0ΓevΘH2On(r), where n is a time integration index going from 0 to Nt total steps, such that the time step length is ht=tp/(Nt+1). The initial condition is ΘH2O0(r)=0. Assuming that the Γimp,H2O is normalized to Γsource according to Eq. (2.7), a simple estimation for the stability of Eq. (4.5) is:

(4.6)ht<1s0(β0Γsource+Γev) The surface site area s0 can be calculated from the stoichiometric ratio between the number of deposited atoms present in the reactant formula to those in the film formula breactant/bfilm, the film mass density ρ, the film molar mass M, the GPC, and Avogadro’s constant N0 as [109]

(4.7)s0=bAbfilmMN0GPCρ. For the H2O step of the ALD of Al2O3, bH2O/bAl2O3=1/3, and the remaining factors can be experimentally measured and are usually reported. It is important to note that Eq. (4.7) is only an approximation to the surface site area, since, e.g., the effects of steric hindrance [68] are not included.

Although the discretization from Eq. (4.5) solves the issue of the coupling between reactant transport and surface chemistry, it does so at the cost of performing the transport calculation Nt times per advection step. In topography simulations of conventional processes such as reactive ion etching (RIE), the involved Langmuir equations are assumed to be on the steady-state [41, 43], thus only a single transport calculation is required per advection step. Therefore, the use of an accurate top-down pseudo-particle tracking approach to Γimp, presented in Section 2.3.3, can be exceedingly demanding for computational resources. Nonetheless, the use of Monte Carlo pseudo-particle tracking has been reported for ALD [101, 155].

To avoid this large computational cost and, therefore, to enable more efficient investigations of the effect of the parameters an one-dimensional (1D) diffusive model can be used. The approach of combining Knudsen diffusion and Langmuir kinetics has been first introduced for ALD by Yanguas-Gil and Elam [30, 111, 124]. However, their approach assumes irreversible adsorption, i.e., Γev=0. According to the formulation of Chapter 3, the diffusive reactive transport ordinary differential equation (ODE) in the preferential transport direction z[0,L] can be written as follows:

(4.8)Dd2Γimpdz2=sβ0(1Θ(z))v4Γimp(z)(4.9)Γimp(0)=Γsource(4.10)DdΓimpdz|z=L=β0(1Θ(L))v4Γimp(L)

The formulation in Eq. (4.8), differently than that from [124], enables more versatility in the calculation of D. The diffusivity can be calculated not only via the standard Knudsen diffusivity from Eq. (3.24), but it can also include the geometric factors for rectangular trenches discussed in Section 3.3, as well as transitional flow through the Bosanquet interpolation formula from Eq. (3.44). The thermal speed v is calculated using the reactor temperature and reactant molar mass using Eq. (3.4). The source flux Γsource is inferred from the reactant partial pressure using the Hertz-Knudsen relationship from Eq. (3.7).

Equations (4.8) to (4.10) require a numerical solution. They are solved using a central finite differences scheme by dividing the z domain in Nz+1 slices of size hz=L/(Nz+1) and index k. The following tridiagonal equation system is obtained:

(4.11)Γk+1[2+hz23v2D2β0(1Θk)]Γk+Γk1=0(4.12)Γ0=Γsource(4.13)[2+(hz23v2D2+hz2vD)β0(1ΘNz)]ΓNz+2ΓNz1=0 This tridiagonal system is solved using the general banded matrix solver from LAPACK [156] with the OpenBLAS library [157].

The crucial assumption behind phenomenological modeling is that the model parameters, namely β0 and Γev, are strongly correlated to the reactor conditions. Therefore, before moving to the parameter investigations with support of experimental data in Section 4.4, it is important to investigate the effects of the model parameters over conformality. To quantify conformality, two calculations are performed for each pair of parameters. First, the saturation coverage Θsat is calculated at z=L as the steady-state convergence (dΘ/dt=0) of Eq. (4.5). Then, a second calculation is performed, now to calculate the required tp to achieve 95% of Θsat. A region of the parameter space is shown in Fig. 4.3, calculated with standard Knudsen diffusivity for a cylinder with d=1µm and L=100µm, and a fictitious reactor chemistry with s0=2×1019m2 and Γsource=1024m2s1.

Figure 4.3 indicates that the required tp is mostly impacted by the β0 parameter, as the gradient varies little across the y axis. Instead, the impact of Γev is on restricting the maximum achievable Θsat, as seen in the upper left-hand side of the figure. That is, Γev severely limits the maximum AR which can be conformally processed by an ALP reactor configuration, even for values as low as one millionth of Γsource.

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Figure 4.3: Investigation of phenomenological model parameters (β0 and Γev) on the required tp to reach 95% of Θsat for a cylinder with d=1µm and L=100µm and a fictitious chemistry with s0=2×1019m2 and Γsource=1024m2s1. Reprinted from Aguinsky et al., arXiv: 2210.00749, (2022) [145], © The Authors, licensed under the CC BY-NC-ND 4.0 License, https://creativecommons.org/licenses/by-nc-nd/4.0/.