Abstract
A comprehensive systematic method for chemical vapor deposition modeling consisting of seven well-defined steps is presented. The method is general in the sense that it is not adapted to a certain ...
Highlights
Chemical vapor deposition (CVD) processes comprise a vast range of physical and chemical phenomena acting at different time and length scales extending over 10−15 orders of magnitude; from chemical reactions to fluid flow and heat transfer, all influencing the properties and quality of the final coating.[1]
We present a systematic method for CVD modeling where input kinetic data, from literature or from quantum chemical modeling, is combined with computational fluid dynamics to generate a map of the CVD chemistry that can mimic experimental data without applying correction factors
To enable in-depth studies of CVD through modeling, we suggest a systematic approach as summarized in Table 1, with the details given in the Methods section
Summary
Chemical vapor deposition (CVD) processes comprise a vast range of physical and chemical phenomena acting at different time and length scales extending over 10−15 orders of magnitude; from chemical reactions to fluid flow and heat transfer, all influencing the properties and quality of the final coating.[1]. The CVD process is in many aspects a black box, for which the tuning of input parameters such as precursor flow rates and temperature settings control the final deposition output This black box can, be unlocked through modeling and simulations that provide detailed information impossible to obtain experimentally.[2,3] Simulation data can often be presented as distributions rather than (a few) points as in the case of measurements, and combining simulation data from a complete and detailed model with experimental data enables enhanced analysis possibilities and a deeper understanding, moving toward a knowledge-based process development. None of them use a systematic method for selecting which reactions shall be included This points to the problem: when data from different literature sources are compiled into a new model, how can one be sure that the resulting chemical model is accurate and relevant for its purpose?. We present a systematic method for CVD modeling where input kinetic data, from literature or from quantum chemical modeling, is combined with computational fluid dynamics to generate a map of the CVD chemistry that can mimic experimental data without applying correction factors
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