Modelling of Transport Phenomena and Detailed

Basically, two types of models for CVD have emerged: (i) Detailed ai initio chemistry models, involving large numbers species and elementary reactions in the gas phéise and at the surface, have been developed for various CVD processes, e.g. [5, 6, 7, 8]. They have been used in combination with simple, one-dimensional models for the fluid flow in idealised reactor configurations. This type of modelling provides a fundamental insight in the bóisic chemical phenomena and may lead to an increased understanding of the influence of operating conditions on detailed process characteristics, such as selectivity, particle formation and dopant incorporation. The idealised flow modelling makes this approach of limited value in the design of actual reactors. (ii) On the other hand, models for the multidimensional transport phenomena in realistic reactor configurations have been developed, which are usually combined with simple, semi-empirical descriptions of the overall deposition chemistry, e.g. [9, 10, 11, 12]. This type of modelling can lead to improved reactor designs with respect to thickness uniformity, gas consumption, prevention of dead zones etc. However, semiempirical lumped chemistry models do not allow for the prediction of more detailed process characteristics such as mentioned above. In addition, they can only be applied at operating conditions for which empirical growth rate data are available.

Modelling of Transport Phenomena and Detailed Chemistry in Chemical Vapour Deposition Equipment K.J.Kuijlaars, CR. Kleijn * and H.E.A. van den Akker Kramers Prins

Laboratorium voor Fysische Technologie Delft University of Technology Bernhardlaan 6, 2628 BW Delft, The Netherlands

Abstract A numerical computer modelling study has been made of low pressure tungsten Chemical Vapour Deposition from WFs and H, in a smgle wafer reactor. A detailed description for the multiple reaction mnlti-spec.es chem.stry, based on the model developed by Arora and PoUard, has been combined with a multidimensional transport model for the conservation of mass, momentum energy and chemical species. The predicted deposition rates at ôth very low and very high WFe concentrations, where more simple semi-empirical chemistry models loose their vabdity, are in fair agreement with experiments. The model was used to study the influence of process conditions and reactor geometry on the formation of tungsten sub-fluorides, which are believed to be responsible for selectivity loss. Thus, the potentials of combined transport and detailed chemistry modeUing in reactor and process optimisation are demonstrated.

1

Introduction

The importance of predictive modelling as a design tool for C V D (chernical vapour deposition) processes and reactor equipment in the micro electronics - d u s t r y has been recognised since the early 1980's [1, 2). A large number of mathematical models have been developed since [3]. Especially numerical computer simulations have proven to be powerful and flexible modelling tools. Today, these mode s replacing the more traditional trial-and-error design methods [4]. Thus, compu added design contributes to better reactor and processes, reduced costs and shorter design cycles. • presenting

author,

io whom correspondence

should

be

Until recently, the integration of these two modelling approaches was prohibited by limitations in computer capacity and by a lack of suitable computer codes and numerical techniques. A first example of a combined detailed chemistry and transport model was published by Jensen and coworkers [13]. However, they used the so-called dilute mixture approach, allowing for the decoupling of flow and chemistry when reactants and reaction products are highly diluted in a carrier gas. In low pressure processes with high reactant and reaction-product concentrations, the gas flow and the chemistry are strongly coupled. In [14] a non-dilute flow model was combined with relatively detailed gas-phase chemistry model. The capacity of present computers now allows for the coupling of detailed gasphase and surface chemistry models with multi-dimensional models for transport phenomena. There has also been a rapid progress in the development of efiicient numerical algorithms and software packages (both academic and commercial) suited for this type of modelling. Such combined models can be used to study the influence of reactor design on spatial variations in e.^. selectivity, particle formation and dopant incorporation. This opens the door to actual predictive modelling and computer added process and reactor design. In this paper we will describe a general approach for comprehensive chemistry and transport modelling, which can be used as a tool in process optimizatation and reactor design. The model equations have been solved numerically with an academic CVD simulation code, as well as the proprietary CVD simulation package PhoenicsCVD. A detailed modelling study of tungsten CVD in a single-wafer cold-wall reactor will illustrate the potentials of this type of modelling.

addressed

Electrochemical Society Proceedings Volume 95-4

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475

2

Tungsten C V D

In the last decade there has been a considerable interest in the use of chemical vapour deposited tungsten as a material for metallisation in ULSI technology [15]. Tungsten can be used as shunt material, diffusion barrier, and for the filling of contact holes and via's. I t is suitable for metallisation purposes because of its low bulk resistivity, low contact resistance to TiSii, chemical stability, low electro-migration and favourable thermal expansion. For the filling of via's and contact holes, both blanket and selective deposition processes are being used. In blanket processing, a subsequent backetching step is needed, which is avoided in selective processing, where deposition takes place on silicon and metal surfaces, but not on oxides and other insulators. The two most common processes use WFe as tungsten precursor and H2 or SiHi as reducing agents. In both cases the process is preceded by a self limiting deposition step, in which WFe is reduced by solid silicon [16]: 2WFe{g) + 35i(^)

2W{s) -f iSiF.ig)

(1)

For the hydrogen reduction of WFe, the chemistry can be described by the overall deposition reaction WFe + 3//2 W{s) -f QHF (2) For the common process conditions used for metallisation in micro-electronics (600 - TSO/f.lO^ - 10^Pa), several authors have found [17, 18, 19, 20] that the deposition rate depends on the hydrogen partial pressure and the temperature, and is virtually independent of WFe for a sufficiently high WFe concentrations, according to: R = c^[PwFA°\PHA'"exr{-^)

(3)

where PWF^ and P^j are the tungsten-hexafluoride and hydrogen partial pressures respectively. For the activation energy EA, values between 67 and 73 kJ/mole have been reported. Eq. 3 has been used successfully in tungsten CVD reactor design and process optimisation [21, 22, 23). A problem with the assumption of zero order kinetics in WFe is that i t cannot be valid for very low WFe concentrations. This range is of interest for the description of the deposition inside contact holes and via's. Therefore, it has been suggested [21, 23] that the overall growth mechanism is determined by the sequential gas-phase diffusion of reactants to the surface and zero order heterogeneous reaction. This led to good agreement with experimental growth rates even at very low WFe concentrations. Others proposed that the rate order is small but not zero, at least at low WFe concentrations. Van der Putte [20] assumed a change from zero order to 1/6 order at low WFe concentrations, owing to changes in the reaction mechanism. Theoretical studies [19, 24] of the kinetics of the surface chemistry of the hydrogen reduction


476

of WFe showed that an l / 6 t h order dependence in the WFe partial pressure can be expected if the desorption of HF from the surface is the rate limiting step. Most experimental studies failed to give evidence for a non-zero order, but more recent measurements [25, 26] show a clear non-zero order dependence outside the regime where transport limitations can be expected. Further, the assumption of zero order kinetics in WFe appears to lose its validity at large WFe:Hi ratios. Creighton [27] showed that for these conditions the rate order m WFe can become negative. This was qualitatively explained using a LangmuirHinshelwood reaction mechanism with competitive adsorption. Thus, there are ample questions regarding the validity and applicability of the lumped overall chemistry model formed by Eqs. 2 and 3. A perhaps even more important shortcoming of this model is that it does not account for the formation of byproducts and intermediates, such as HF and tungsten-subfluorides (WF,,., x=3,4,5). These species are generally believed to play an important role in the rriechaiiisms underlying phenomena such as "encroachment", "worming" and selectivity loss [15]. The above illustrates the need for a more fundamental model of the elementary reactions in tungsten CVD, such as was developed by Arora and Pollard [28]. Unlike empirical lumped rate expressions, such an ab initio detailed model should be able to predict the process behaviour outside the range of process conditions for which experimental data are available. Moreover, it should be able to predict the formation of by-products and intermediates and their influence on the process. Arora and Pollard applied this model in 1-dimensional simulations of an impinging jet reactor and concluded that i t satisfactorily predicts growth rates for a large range of process conditions. However, 1-dimensional simulations cannot be used in actual reactor design. The purpose of the present work was therefore to combine the Arora chemistry model with multidimensional reactor simulations. Thus, this paper will show how combined detailed chemistry and transport models can be used to gain msight in the influence of process conditions and reactor geometry on deposition rates, uniformities and by-product formation.

3

Chemistry Model

Using statistical mechanics, transition state theory and bond dissociation enthalpies, Arora and Pollard [28] estimated the rate constants for a large set of elementary reactions in the gas and at the surface. Thus, the major reaction pathways were identified, and the reaction model was reduced to 11 species and 8 reactions in the gas phase and 9 species and 16 elementary processes at the surface, as shown in tables I and I I . Sl and S2 represent the adsorption of WFe and the dissociative adsorption of H2. In S9-S12 the adsorbed WFe loses fluorine atoms in three consecutive steps. In S13, WF3 dissociates to form solid tungsten. In S5-S8, HF is formed and desorbed. S3 and S4 represent the desorption of WF^ and WF^. The deposition rate is controlled by two rate limiting steps: the removal of '^F{a) from the surface (S8) and by the


477

conversion of ^ ^ 4 ( 0 ) to ^WFsia) ( S l l and S12). For the range of common process conditions, the influence of gas-phase reactions on the model results can be ignored. For the present study, the Arora model was initially implemented without modifications. The elementary reactions, backward rate constants and the thermo chemistry data for the calculation of equihbrium constants were taken directly from [28). However, a number of problems were encountered with the Arora model as published. In order to obtain realistic growth rate predictions, the rate constants of S2 and S8 as published in [28] had to be adjusted. Also, the standard entropies for W2FS and W2F10 as pubhshed in [28] appeared to be probably erroneous [29]. As a result, unrealistic equilibrium constants for G4 and 05 were obtained. Therefore, these reactions were omitted in the present study. This is described in more detail in [30]. Thus, in the present study a "modified Arora model" was used, which nevertheless closely mimics the model behaviour reported in [28]. As will be shown in Section 5, the modified model also predicts experimental growth rates in a large range of process conditions with reasonable accuracy. However, the changes in the model are not well supported, and obviously such ad hoc modifications somewhat conflict with the basic ideas underlying ab initio chemistry modelling. A further evaluation and refinement of the model are the topic of ongoing study.

4

Transport Model

The transport model for the gas flow and transport phenomena in CVD reactors has been described in Refs. [31, 21, 22]. This model assumes steady-state, continuum conditions, laminar gas flow and the validity of the ideal gas law. Thus, the gas flow and heat transfer in an N component gas mixture are described by the continuity equation, the conservation equations for momentum, or the Navier-Stokes equations, and the energy equation. The flow equations are coupled to N-1 species balance equations. The multi-component diffusion is modelled through the Stefan-Maxwell equations and thermal diffusion is accounted for. I t has been shown [32], that approximate, Fick descriptions of ordinary diffusion give incorrect results for the gases and process conditions studied. The transport properties of the individual gas species and the binary diffusion coefficients are calculated from the Lennard-Jones interaction parameters using k i netic gas theory. Semi-empirical mixture rules are used for calculating the transport properties of the gas mixture as a function of pressure, temperature and mixture composition. The coupled partial differential equations, together with the appropriate boundary conditions, were discretized using a finite volume approach [33]. The resulting algebraic equations were solved iteratively using the line-by-line T D M A solution procedures that are common in finite volume flow modelling. The SIMPLE algorithm was used to link the continuity equation to the Navier-Stokes equations.


478

In the iterative solution procedures that are commonly used in finite volume flow modelling, the various coupled conservation equations are mathematically being decoupled. Coupling is then accounted for through repetitive iteration. This approach however is not suited when a large set of conservation equations for the various species in a reacting mixture are being solved. If multiple reactions take place, and if these reactions have differing reactions rates and therefore different characteristic time scales, the resulting set of discretized species convection-diffusion-reaction equations is numerically stiff. For a moderate stiffness, the equations can still be solved in the traditional way, but small false time steps must be used. I f the stiffness is large, this approach leads to excessively large numbers of iterations. Therefore, the discretized equations for the gas species concentrations in each grid point were treated as a set of non-linear algebraic equations and solved on a Jacobi point-by-point basis by Newton iteration. This procedure allows for the solution of stiff chemistry without using small false time steps. I t does however lead to a weaker coupling between neighbouring grid points, which may deteriorate convergence if the transport terms are not small compared to the reaction terms. In such cases the combined use of traditional decoupled line-by-line solvers and the coupled point-bypoint procedure proved to be efficient. Similar stiffness problems occur at the surface. The equations for the surface coverages in each grid point at the surface were treated as a set of ordinary differential equations and integrated from t = 0 to t=oo with the VODE ordinary differential equation solver [34]. Although only time independent solutions were sought, the solution of the steady state equations with Newton iteration proved to be impractical due a lack of accurate initial estimates for the surface coverages. Furthermore, the relatively small number of grid points at the surface allows for the use of a computationally more expensive solution procedure. The above CVD model and solution procedures were implemented in an academic computer code [12, 22] as well as in Phoenics-CVD, a customised version of the CFD code Phoenics [35]. The results obtained with these codes were essentially identical. For most calculations a grid of 42 cells in the axial direction and 28 cells in the radial direction was used. The independence of the numerical solution on further refinement of the computational grid was tested by using a grid of 60 cells in the axial direction and 40 cells in the radial direction. This had negligible effects on the solution. The calculations were carried out on an HP 735 workstation. To reach a converged solution ca. 5000 iterations were needed, consuming several hours of calculation time.


479

5 5.1

Results Comparison of predicted growth rates with experimental results

Two-dimensional simulations of tungsten LPCVD in a single-wafer cold-wall reactor were performed. In Figure 1 the predicted growth rates, obtained with the present detailed chemistry model as well as with the lumped semi-empirical model (Eqs. 2 and 3), are compared to two different experimental datasets [21, 26]. In Figures l a and l b experimental results for the growth rate as a function of the WFe inlet concentration are shown. The experiments show a transition between two rate limiting processes. For high WFe concentrations a small dependence of the deposition rate on the WFe concentration is found, whereas for low WFe concentrations this dependence is strong. This is often interpreted as a transition from kinetically limited growth to transport limited growth. Indeed the semi-empirical model, which is based on this assumption, appears to predict the transition observed in [21] (Fig. la) with reasonable accuracy. In contrast, it can be seen that for the detailed chemistry model the agreement is less satisfactory. The detailed model predicts a slower transition between the two regimes and no zeroth order behaviour in the WFe pressure is seen at higher WFe concentrations, apparently in disagreement with the experiments from [21]. A different picture is obtained when the same comparison is made for the data in Fig. l b [26]. Here, a more detailed study is made of the deposition rate in the transition regime. For these data, the detailed model seems to be much more accurate than the semi-empirical model. When comparing the deposition uniformity predicted by the two models to the experimental data in Fig. Ic, the simple chemistry model appears again to perform slightly better than the detailed model. The latter predicts a too good uniformity for low WFe concentrations and a too low uniformity for high WFe concentrations. I t is clear that this discrepancy between the two independent experimental data sets needs to be clarified before definitive conclusions on the accuracy of the detailed chemistry model versus the lumped model can be made. However, in general i t appears that for simple growth rate and uniformity modelling the use of a simple lumped chemistry model is more appropriate than the application of a much more detailed chemistry model.

5.2

Subfiuoride formation

In contrast, a detailed chemistry model, predicting the formation of by-products and intermediates, is necessary when e.g. a phenomenon such as selectivity loss is being studied. Tungsten CVD is attractive for most applications because of the selective nature of


480

the process, i.e., deposition occurs on silicon and tungsten, but not on SiOj and other insulators. However, this selectivity may be lost as tungsten nucleates on the SiOi surface. In some studies HF is suspected of initiating the selectivity loss [18, 36, 37]. Other studies (38, 39] failed in establishing a link between the HF partial pressure and the degree of selectivity loss. Instead, a gaseous tungsten sub-fluoride, probably WFs, is suggested to adsorb and disproportionate on the SiOj surface [23, 38]. In order to reach a good selectivity, the build-up of high concentrations of WFj:, desorbed from the tungsten surfaces, should be avoided. Therefore, we used the detailed chemistry model to study on the influence of process conditions and reactor geometry on the formation of tungsten subfluorides in a single-wafer cold-wall LPCVD reactor. In our simulations, tungsten is grown selectively on a 10 cm diameter wafer with a, reactive area fraction of 0.01. The wafer is surrounded by a 1 cm wide area on which non-selective tungsten growth takes place. Figure 2 shows contours of the predicted mole fraction of WFe, as well as the gas concentrations of some other important intermediates just above the wafer surface. The intermediate species WFs, which is formed mainly on the non-selective ring around the wafer, diffuses radially inwards towards the wafer centre. A factor 2 difference in WFs gas-phase concentration between the wafer edge and the wafer centre can be observed. In figure 3 various ways of reducing WFs build-up are studied. By increasing the total gas flow in the reactor from 1 to 10 slm (Fig. 3a), the WFs concentration at the wafer edge drops by a factor of 1.4, whereas in the centre, the WFs concentration drops by a factor of 6. These results agree with the findings of Werner et al. [40]. Thus, an increase in the total flow seems to be an eff'ective (but expensive) way for reducing selectivity loss. It has been noted that selectivity is improved at low temperatures and low WFe concentrations [41, 18, 37, 36]. This seems to agree with the findings in [28], that the production of the intermediates WFs and WF4 is smallest at low wafer temperatures and WFe pressures. Figures 3b and 3c confirm these results. However, whereas a reduction in the WFe concentration strongly reduces subfluoride formation' while having little influence on the deposition rate, a reduction in temperature is accompanied by a strong reduction in deposition rate. Thus, in contrast to blanket processing where low flowrates and high WFe concentrations are favourable [21], the use of high total flows and low WFe concentrations appears to be favourable for selective processing. Figures 4 and 5 show the influence of modifications in the reactor geometry on the WFs concentration. In figure 4 it is shown that the application of a baffle that reaches to near the wafer surface can prevent the radial inward diffusion of subfluorides from the non selective area outside the wafer. This has also been found by Werner and coworkers [40]. Figure 5 shows the favourable eff'ect of a reduction of the reactor volume.


481

6

Conclusions

It has been shown that a detailed model for the tungsten CVD chemistry, including a considerable number reactions and species in the gas-phase and at the surface, can be combined with a multi-dimensional transport model. This opens the door to the prediction of process characteristics outside the range of validity of empirical rate expressions. Also, the influence of reactor design and process conditions on the formation of reaction intermediates and by-products can be studied. Thus, this study has shown the potentials of combined chemistry and transport modelling in tungsten CVD equipment and process design. The modified chemistry model as presented in this paper fairly accurately predicts growth rates over a wide range of process conditions. The predicted dependence of the growth rate on the WFe concentration for high WFe concentrations however, seems not to be in agreement with experimental data, and leads to an under-prediction of the growth uniformity under these conditions. Based on two different experimental datasets, i t is not quite clear yet how well the model performs in the transition regime. However, in general the detailed modelling approach seems especially useful for the design of processes outside the range of common operating conditions, and for the study of by-product and intermediate formation. When the primary interest is in the influence of reactor geometry on growth rate and uniformity under common operating conditions, the use of a simple empirical rate expression seems more appropriate. The model predictions indicate that especially a reduction in the WFe concentration is an effective way of reducing subfluoride formation. Relatively simple geometric variations in the reactor design can also lead to significant improvements.

Acknowledgements

[3] C R . Kleijn. Chemical vapor deposition processes. In M . Meyyappan, editor, Computational Modeling in Semiconductor Processing, chapter 4, pages 97-229. Artech House, Boston, 1995. [4] J. Ignacio Ulacia F. and Christoph Werner. Equipment simulation: Part I . Solid State Technology, pages 107-113, 1990. [5] M.E. Coltrin, R.J. Kee, and G.H. Evans. A mathematical model of the fluid mechanics and gas-phase chemistry in a rotating disk chemical vapor deposition reactor. Journal of ihe Electrochemical Society, 136 (3):819-829, 1989. [6] M . Tirtowidjojo and R. Pollard. Elementary processes and rate-limiting factors in MOVPE of GaAs. Journal of Crystal Growth, 77:108-114, 1988. [7] C.J. Giunta, J.D. Chappie-Sokol, and R. Gordon. Kinetic modeling of the chemical vapor deposition of silicon dioxide from silane or disilane and nitrous oxide. Journal of ihe Elecirochemical Society, 137(10):3237-3253, 1990. [8] M . Masi, H. Simka, K.F. Jensen, T.F. Kuech, and R. Potemski. Simulation of carbon doping of GaAs during MOVPE. Journal of Crystal Growth 124-483-492 1992. [9] H . K . Moff'at and K . F . Jensen. Three-dimensional flow eff'ects in silicon CVD in horizontal reactors. Journal of ihe Elecirochemical Society, 135 (2):459-471 1988. [10] D . I . Fotiadis. Two- and three-dimensional finite element simulations of reading flows in chemical vapor deposition of compound semiconductors. PhD thesis. University of Minnesota, Minneapolis, USA, 1990. [11] J. Ouazzani and F. Rosenberger. Three-dimensional modelling of horizontal chemical vapor deposition. Journal of Crystal Growth, 100:545-576, 1990.

This work forms part of the ESPRIT project 7161 "ACCESS-CVD" (Application of a Customised CFD Environment to the Study and Simulation of Chemical Vapour Deposition) and was made possible by the support of the EU. We wish to thank Professor R. Pollard for his assistance in clarifying various issues regarding the chemistry model.

[12] C R . Kleijn and C J . Hoogendoorn. A study of 2- and 3-d transport phenomena in horizontal chemical vapor deposition reactors. Chemical Engineering Science, 46 (l):321-334, 1991.

References

[13] K . F . Jensen, D . I . Fotiadis, and T.J. Mountziaris. Detailed models of the MOVPE process. Journal of Crystal Growth, 107:1-11, 1991.

[1] G. Wahl. Hydrodynamic description of CVD processes. Thin Solid Films, 40:1326, 1977.

[14] C R . Kleijn. A mathematical model of the hydrodynamics and gas-phase reactions in silicon LPCVD in a single-wafer reactor. Journal of ihe Elecirochemical Society, 138 (7):2]90-2200, 1991.

[2] K . F . Jensen. Micro-reaction engineering applications of reaction engineering to processing of electronic and photonic materials. Chemical Engineering Science, 42 (5):923-958, 1987.

[15] John E.J. Schmitz. Chemical Vapor Deposition of Tungsten and Tungsten Silicides. Noyes, Park Ridge, New Yersey, 1992.


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483

[16] G.J. Leusink. Growth and Properties of CVD-W Films for Microelectronic plications. PhD thesis, Delft University of Technology, 1994.

Ap-

[17] E.K. Broadbent and G.L. Ramiller. Selective low pressure chemical vapor deposition of tungsten. Journal of ihe Electrochemical Society, 131 (6):1427-1433, 1984. [18] Y . Pauleau and Ph. Lami. Kinetics and mechanism of selective tungsten deposition by LPCVD. Journal of ihe Elecirochemical Society, 132 (ll):2779-2784, 1985. [19] C M . McConica and K. Krishnamani. The kinetics of LPCVD tungsten deposition in a single wafer reactor. Journal of ihe Electrochemical Society, 133 (12);2542-2548, 1986. [20] P. van der Putte. The reaction kinetics of the Hi reduction of WFe in the chemical vapour deposition of tungsten films. Philips Journal of Research, 42:608-626, 1987. [21] C R . Kleijn, C J . Hoogendoorn, A. Hasper, J. Holleman, and J. Middelbeek. Transport phenomena in tungsten LPCVD in a single-wafer reactor. Journal of the Elecirochemica Society, 138:509-517, 1991. [22] Chris R. Kleijn and Christoph Werner. Modeling of Chemical of Tungsten Films. Birkhauser, Basel, 1993.

Vapor

[30] K.J. Kuijlaars, C R . Kleijn, and H.E.A. van den Akker. A detailed model for low pressure cvd of tungsten. Submiited io Thin Solid Films, 1995. [31] Chris Kleijn. Transport Phenomena in Chemical PhD thesis, Delft Iniversity of Technology, 1991.

Vapor Deposiiion

Reactors.

[32] K.J. Kuijlaars, C R . Kleijn, and H.E.A. van den Akker. Multicomponent diffusion phenomena in multiple-wafer chemical vapour deposition reactors. Chemical Engineering Journal, 57(2):127-136, 1995. [33] S.V. Patankar. Numerical heai transfer and fluid flow. Hemisphere Publ Corp Washington DC, 1980. [34] P.N. Brown, G.D. Byrne, and A.C. Hindmarsch. VODE: a variable coefficient ODE solver. SIAM Journal of Scientific and Staiisiical Computing, 10:10381051, 1989. [35] Phoenics is a product of CHAM Ltd., Wimbledon, London, Great Britain. [36] L.F.Tz. Kwakman, W.J.C. Vermeulen, E.H.A. Granneman, and M . L . Hitchman. A quantitative analysis of the effects of reaction by-products on selectivity during the selective deposition of tungsten. In Victor A. Wells, editor. Tungsten and Other Refractory Metals for VLSI Applications III, pages 141-147, Pittsburgh, 1988. Materials Research Society.

Deposiiion

[23] J.I. Ulacia F., S. Howell, H. Körner, and Ch. Werner. Flow and reaction simulation of a tungsten CVD reactor. Applied Surface Science, 38:370-385, 1989. [24] W . A . Bryant. Kinetics of tungsten deposition by the reaction of WFe and hydrogen. Journal of ihe Elecirochemical Society, 125 (9):1534-1543, 1978. [25] E.J. Mclnerney, T.W. Mountsier, and E.K. Broadbent. A new rate expression for hydrogen reduced tungsten. In Timothy S. Cale and Fabio S. Pintchovski, editors. Advanced Metallizaiion for ULSI Applications in 199S, pages 161-167, Pittsburgh, 1993. Materials Research Society.

[37] C M . McConica. A model for tungsten nucleation on oxide. In Eliot K. Broadbent, editor. Tungsten and Other Refractory Metals for VLSI Applications II, pages 51-57, Pittsburgh, 1987. Materials Research Society. [38] J.R. Creighton. Selectivity loss during tungsten chemical vapor deposition: the role of tungsten pentafluoride. Journal of Vacuum Science and Technology A, 7(3):621-624, 1989. [39] J.R.Creighton. A mechanism for selectivity loss during tungsten CVD. of the Elecirochemical Society, 136(l):271-276, 1989.

Journal

[26] T . G . M . Oosterlaken. To be published.

[40] Ch. Werner, J.I. Ulacia, C. Hopfmann, and P. Flynn. Equipment simulation of selective tungsten deposition. Journal of the Elecirochemical Society, 139 (2):566-574, 1992.

[27] J.R. Creighton. The surface chemistry and kinetics of tungsten chemical vapor deposition and selctivity loss. Thin Solid Films, 241:310-317, 1994.

[41] J.O. Carlsson and M . Boman. Selective deposition of tungsten - prediction of selectivity. Journal of Vacuum Science and Technology A, 3 (6):2298-2302, 1985.

[28] R. Arora and R. Pollard. A mathematical model for chemical vapor deposition influenced by surface reaction kinetics: Application to low pressure deposition of tungsten. Journal of the Electrochemical Society, 138 (5):1523-1537, 1991. [29] R. Pollard. Personal communications.


484


485

10

A

I I I iiiiij—r-| I

chemistry model

—I I I

-1

—r-n

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4 0 0 P a , 703 K

Table I . gas-phase reactions.

Gl G2 G3 G4 G5 G6 G7 G8

WFs + F^ 2H ^ H2 2F^F2 2WFs

^

WFe

W2F1Q

2WFi ^ W^Fi HWFs + WFs + H2 H + HF + WFs - WFe + H2 WFe + WFi ^ 2WFs

1

0.1

100

20

0 (b)

40

60

80

100

WFg flow (scorn)

r

1

Table I I . surface reactions, (a) denotes adsorbed species; the numbers 1, 2 and 4 in front of the species names denote the number of bonds; v denotes a dangling bond or vacancy; the appearance of 2v above the ^ sign denotes the fact that 2 extra dangling bonds are needed at the surface for the reaction to proceed in either direction.

10

partial pressure (Pa)

(S)

40 seem WFe

-E 0 5 Sl S2 S3 S4 S5 S6 S7

1.3 seem WFe

WFe-\-2v^^WFe{a) WFs + 2v^ HVFsia) WF4 + 2v^ ^VFila) HF + 3v^ 'H{a) + ''F{a) HF + 3v^^H{a)-\-'Fia) HF + 6v^''H{a) + ''F{a)

(C)

S8 S9 SIO Sll S12

HF + 'H{a)^ 2F(a) -1- ^WFs(a) ''Fia) + HVFiia) ^F{a) + ^WFsia)

H2-\-'F(a) ^ ^WFe{a) + 2v ^ ÎVFsCa) -1- 2v ^ ^WFîa) -f 2t;

2F(a) -1- ^WFîa)

^ "WF.ia)

S13 S14 S15 S16

W{s) + 'Fia) + 2'Fia) 'Hia) + v^''Hia) i F ( a ) - t - u ^ 2f(a) 'Fia) + 2v^ "Fia)

+ 2v


-L 0.02

0 03

0.04

radius (m)

figure 1: Growth rate predictions and experimental results. (a) Average deposition rate on a 3 in. wafer as function of the WFe inlet pressure at different wafer temperatures and total pressures. Qu, = 1000 seem. A n Ar flow was used to keep the total flow at 1200 seem. Detailed chemistry model (—), semiempirical chemistry model ( ) and experimental results (o, • ) [21]. (b) Average deposition rate on a 4 in. wafer as function of the WFe flow at different wafer temperatures. Total pressure is 533 Pa, QH, = 825 seem. Detailed chemistry model (—), semi-empirical chemistry model ( ), experimental results ( o , A , D) [26]. (c) Growth uniformity on a 3 in. wafer, for two different WFe flows. Total pressure is 133 Pa, Qif, = 1000 seem. An Ar flow was used to keep the total flow constant at 1200 seem. Detailed chemistry model (—), semi-empirical chemistry model (- - -) and experimental results (o, • ) [21].

+ 2v

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JL 0.01

486


487

r—

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"•••lOyoWFe---

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—

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figure S: Predictions of by-products and intermediates. Selective deposition on 1 percent of the area of a 10 cm diameter wafer. Nonselective deposition on a 1 cm wide ring around the wafer. Total pressure=133 Pa, wafer temperature=573 K, 100 seem WFe + 900 seem . Upper figure shows contours for the WFs mole fraction, lower figure shows gas species mole fractions just above the wafer surface.


488

figure 3: Influence of process conditions on deposition rate (—) and sub-fluoride build-up (- - - ) . Process conditions as in figure 2, unless stated otherwise. (a) Influence of increased total flow-rate. (b) Influence of decreased WFe inlet concentration. (c) Influence of decreased wafer temperature.


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figure 5: Influence of reactor geometry on deposition rate (—) and sub-fluoride buildup ( ) . Process conditions as in figure 4.


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