Gas-phase kinetics in the atmospheric pressure chemical vapor deposition of silicon from silane and disilane

Giunta, Carmen J.; McCurdy, Richard J.; Chapple‐Sokol, J.; Gordon, Roy G.

doi:10.1063/1.345792

Cited by 68 publications

(50 citation statements)

References 50 publications

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“…Reaction mechanisms were constructed using the approaches used previously at 1023 K for initial H 2 /SiH 4 molar ratios of both 99:1 (HAS/PRB) and 90:10 (HAS/RB) at a higher temperature of 1200 K. Figure 5a shows the results of solving the full Si 7 mechanism for an initial H 2 /SiH 4 molar ratio of 99:1 at 1200 K. Similar to the same initial composition at 1023 K, larger species were present in relatively lower concentrations in general. The concentrations of Si 5 H x , Si 6 H x , and Si 7 H x were all very close, which indicates that the critical particle size may be somewhere near Si 6 or Si 7 , and it is very likely that it is smaller than the critical particle size at 1023 K. The particle rate-based approach coupled with the heavy atom shell technique was also carried out, and the a The critical weighting factor, which is defined as the value where ∆ is less than 5% for each particle size up to Si7, is shown in italics. Figure 5.…”

Section: Resultsmentioning

confidence: 79%

“…To evaluate the efficacy of the HAS/RB termination criteria at these reaction conditions, the results using the rate-based termination criterion coupled with the heavy atom shell technique were obtained and are tabulated in Table 7, where the critical weighting factors were found to be uniform for Si 5 , Si 6 , and Si 7 at a value of 0.02 among all values examined. This is different from what was observed for the previous reaction conditions, where the critical weighting factors decreased with increasing heavy atom bound.…”

Section: Resultsmentioning

confidence: 99%

“…1,11 For larger silicon hydrides, the mechanistic steps were grouped into four families of reversible reactions as tabulated in Table 1 (1) hydrogen elimination (and its reverse reaction, silylene insertion 1-R), (2) silylene elimination (and its reverse reaction, silylene addition 2-R), (3) silene to silylene isomerization (and its reverse reaction, silylene to silene isomerization 3-R), and (4) ring opening (and its reverse reaction, ring formation by intramolecular insertion 4-R). These reaction families were developed on the basis of the studies of Swihart and Girshick 1 and Giunta et al 6 The rate parameters used in these reaction families were estimated using linear free-energy relationships (LFERs). Literature values of preexponential factors were applied, 1 and the activation energies, E a , were estimated from the heats of reactions, ∆H rxn , using the EvansPolanyi relationship 23,24 where E o is the intrinsic barrier to reaction and R is the reaction transfer coefficient.…”

Section: Model Developmentmentioning

confidence: 99%

“…However, the userspecified weighting factor is only applied to the larger molecules for which no critical is known and for sizes for which critical,i is less than the specified by the user; for example, if a weighting factor of 1 × 10 -8 is specified by the user for the same reaction conditions discussed earlier, then this weighting factor is only applied to molecules with Si 8 or larger using this approach. The critical weighting factor for Si 7 , that is, 1 × 10 -7 , is used for the molecules with seven silicon atoms, and the critical weighting factor for Si 5 and Si 6 (1 × 10 -5 ) is applied to molecules of Si 6 and smaller. Using this strategy, some of the unnecessary Si 5 H x , Si 6 H x , and Si 7 H x species that were generated when values of a universal below the critical a The critical weighting factor, which is defined as the value where ∆ is less than 5% for each particle size up to Si7, is shown in italics.…”

Section: Termination Criteriamentioning

confidence: 99%

“…4,5 They also predicted the formation of nanoparticles starting from Si 5 H 12 . Giunta et al 6 developed another model with species containing up to 10 silicon atoms. They concluded that silylenes can rapidly insert into other saturated silanes to form larger species.…”

Section: Introductionmentioning

confidence: 99%

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Detailed Kinetic Modeling of Silicon Nanoparticle Formation Chemistry via Automated Mechanism Generation

Wong

Swihart

et al. 2004

J. Phys. Chem. A

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Thermal decomposition of silane can be used to produce silicon nanoparticles, which have attracted great interest in recent years because of their novel optical and electronic properties. However, these silicon nanoparticles are also an important source of particulate contamination leading to yield loss in conventional semiconductor processing. In both cases, a fundamental knowledge of the reaction kinetics of particle formation is needed to understand and control the nucleation of silicon particles. In this work, detailed kinetic modeling of silicon nanoparticle formation chemistry was carried out using automated reaction mechanism generation. Literature values, linear free-energy relationships (LFERs), and a group additivity approach were incorporated to specify the rate parameters and thermochemical properties of the species in the system. New criteria for terminating the mechanisms generated were also developed and compared, and their suitability for handling an unbounded system was evaluated. Four different reaction conditions were analyzed, and the models predicted that the critical particle sizes were Si 5 for an initial H 2 /SiH 4 molar ratio of 90:10 at 1023 K and Si 4 for the same initial composition at 1200 K. For an initial H 2 /SiH 4 molar ratio of 99:1, the critical particle size was larger than or equal to Si 7 for both temperatures, but it was not possible to determine the exact critical particle size because of limitations in computational resources. Finally, the reaction pathways leading to the formation of nanoparticles up to the critical size were analyzed, and the important species in the pathways were elucidated.

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Section: Resultsmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Section: Model Developmentmentioning

confidence: 99%

Section: Termination Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detailed Kinetic Modeling of Silicon Nanoparticle Formation Chemistry via Automated Mechanism Generation

Wong

Swihart

et al. 2004

J. Phys. Chem. A

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Studying of silane thermal decomposition mechanism

Onischuk

Strunin

Ushakova

et al. 1998

Int. J. Chem. Kinet.

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The mechanism of silane thermal decomposition is investigated in a flow reactor. The time dependencies of silane consumption and disilane formation were compared with those parameters of solid product (aerosol particles) such as concentration, total hydrogen content in solid product, and fraction of hydrogen contained in solid product as polyhydride groups (SiH 2 ) n . Silane loss and gaseous product formation were analyzed using a mass spectrometer. The hydrogen content in solid product was analyzed by the methods of IR-spectroscopy and hydrogen evolution. Based on a simple kinetic scheme we qualitatively explained the experimental dependencies of silane conversion and disilane formation, the effective activation energy of the decomposition process, and the amount of polyhydride groups in the solid product on reaction time and initial silane concentration.

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Simulation of Chemical Vapor Deposition Processes

Kleijn¹

2002

Characterization of Materials

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Chemical vapor deposition (CVD) processes constitute an important technology for the manufacturing of thin solid films. Applications include semiconducting, conducting, and insulating films in the integrated circuit industry, superconducting thin films, antireflection and spectrally selective coatings on optical components, and anticorrosion and antiwear layers on mechanical tools and equipment. Compared to other deposition techniques, such as sputtering, sublimation, and evaporation, CVD is very versatile and offers good control of film structure and composition, excellent uniformity, and sufficiently high growth rates. Perhaps the most important advantage of CVD over other deposition techniques is its capability for conformal deposition—i.e., the capability of depositing films of uniform thickness on highly irregularly shaped surfaces. In CVD processes a thin film is deposited from the gas phase through chemical reactions at the surface. Reactive gases are introduced into the controlled environment of a reactor chamber in which the substrates on which deposition takes place are positioned. Depending on the process conditions, reactions may take place in the gas phase, leading to the creation of gaseous intermediates. The energy required to drive the chemical reactions can be supplied thermally by heating the gas in the reactor (thermal CVD), but also by supplying photons in the form of, e.g., laser light to the gas (photo CVD), or through the application of an electrical discharge (plasma CVD or plasma enhanced CVD). The reactive gas species fed into the reactor and the reactive intermediates created in the gas phase diffuse toward and adsorb onto the solid surface. Here, solid‐catalyzed reactions lead to the growth of the desired film. The application of new materials, the desire to coat and reinforce new ceramic and fibrous materials, and the tremendous increase in complexity and performance of semiconductor and similar products employing thin films are leading to the need to develop new CVD processes and to ever increasing demands on the performance of processes and equipment. This performance is determined by the interacting influences of hydrodynamics and chemical kinetics in the reactor chamber, which in turn are determined by process conditions and reactor geometry. It is generally felt that the development of novel reactors and processes can be greatly improved if simulation is used to support the design and optimization phase. In the last decades, various sophisticated mathematical models have been developed, which relate process characteristics to operating conditions and reactor geometry. When based on fundamental laws of chemistry and physics, rather than empirical relations, such models can provide a scientific basis for design, development, and optimization of CVD equipment and processes. This may lead to a reduction of time and money spent on the development of prototypes and to better reactors and processes. Besides, mathematical CVD models may also provide fundamental insights into the underlying physicochemical processes and may be of help in the interpretation of experimental data.

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Gas-phase kinetics in the atmospheric pressure chemical vapor deposition of silicon from silane and disilane

Cited by 68 publications

References 50 publications

Detailed Kinetic Modeling of Silicon Nanoparticle Formation Chemistry via Automated Mechanism Generation

Detailed Kinetic Modeling of Silicon Nanoparticle Formation Chemistry via Automated Mechanism Generation

Studying of silane thermal decomposition mechanism

Simulation of Chemical Vapor Deposition Processes

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