(435a) Detailed Kinetic Modeling of Thermal Decomposition of Silane Via Automatic Mechanism Generation

Authors: 
Slakman, B. L., Northeastern University
Bhoorasingh, P. L., Northeastern University
West, R. H., Massachusetts Institute of Technology
The gas phase thermal decomposition of precursors such as silane is an important step in chemical vapor deposition for semiconductor material fabrication. Understanding the reaction pathways involved in this process can help design processes to better control the quality of the materials produced by CVD. We have extended the open-source software Reaction Mechanism Generator (RMG) [1] so that it can automatically generate detailed kinetic models for silicon hydrides and, eventually, a wide range of silicon-containing species. This work draws from and builds upon previous efforts in automatic mechanism generation for silicon hydrides, specifically in the field of silicon nanoparticle formation [2].

RMG's database was updated to include data for thermodynamics and kinetics important in silane thermal decomposition. Benson's group additivity values for thermochemistry of Si-containing molecules were added [3], and hydrogen bond increment (HBI) values for radical species were derived using quantum chemistry. Four reaction families, which RMG uses to propose reactions and estimate their kinetics, were enabled for silicon hydride chemistry:




Reaction Family

Status

Example Reaction

Hydrogen abstraction

Updated

SiH4 + H. â?? SiH3. + H2

Radical recombination

Updated

SiH3. + H3 â?? SiH4

Silylene insertion

New

SiH2: + SiH4 â?? SiH3SiH3

Silylene-to-silene isomerization

New

SiH3SiH: â?? H2Si=SiH2

Because existing experimental and theoretical kinetic data for some of these families is sparse, additional transition state theory (TST) rates were calculated, using an automated kinetics calculator. The automated TST calculator uses a group additive scheme and distance geometry to predict transition state geometries [4], DFT to optimize and characterize them, and the software package Cantherm to apply conventional transition state theory.

Using the updated database, a detailed model was built for thermal decomposition of SiH4. To validate the RMG-generated model, reactor simulations were compared to flow tube experimental data from Onischuk et al. [5]. Simulation conditions were matched to the experimental conditions, with base conditions of y0(SiH4) = 1.6e-4 in argon, T = 913 K, and P = 39 kPa. The SiH4 concentration profile variation with residence time matches reasonably well with the experimental data within a 20 K uncertainty in temperature. SiH4 concentration dependence on temperature also matches well with experiment.

The experimental SiH4 and Si2H6 concentration profiles, normalized to initial SiH4 concentration, appear to vary with initial SiH4 concentration suggesting a second order dependence on SiH4 concentration. However, the simulation results approximate a first order dependence on SiH4 concentration. Sensitivity analysis and flux analysis were employed to understand and resolve this discrepancy between the initial simulations and experiment.

The newly updated RMG and validated model allows simulation of silicon hydride thermal decomposition at different process conditions, reducing the need for future experiments. Importantly, this work provides a framework for generating detailed kinetic models of other, less well-studied CVD precursors.

References

[1] C.W. Gao, J.W. Allen, W.H. Green, R.H. West. Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Computer Physics Communications, 203, 212-225, (2016) http://dx.doi.org/10.1016/j.cpc.2016.02.013.

[2] H.W. Wong, X. Li, M.T. Swihart, L.J. Broadbelt. Detailed kinetic modeling of silicon nanoparticle formation chemistry via automated mechanism generation. Journal of Physical Chemistry A, 108(46), 10122â??10132, (2004) http://dx.doi.org/10.1021/jp049591w

[3] M.T. Swihart, M. T and S.L. Girshick. Thermochemistry and kinetics of silicon hydride cluster formation during thermal decomposition of silane. Journal of Physical Chemistry B, 103(1), 64â??76, (1999) http://dx.doi.org/10.1021/jp983358e

[4] P.L. Bhoorasingh and R.H. West. Transition state geometry prediction using molecular group contributions. Phys. Chem. Chem. Phys., 17, 32173-32182, (2015) http://dx.doi.org/10.1039/C5CP04706D.

[5] A.A. Onischuk, V.P. Strunin, M.A. Ushakova, V.N. Panfilov. Studying of silane thermal decomposition mechanism. Int. J. Chem. Kinetics, 30(2), 99-110, (1998) http://dx.doi.org/10.1002/(SICI)1097-4601(1998)30:2<99::AID-KIN1>3.0.CO;2-O