论文标题
在化学蒸气沉积期间处理碳材料的化学蒸气沉积期间的略有分辨的RO振动光谱：等离子体温度计的机器学习方法
Processing slightly resolved ro-vibrational spectra during chemical vapor deposition of carbon materials: machine learning approach for plasma thermometry
论文作者
论文摘要
提出了一种用于确定旋转（$ t_ {rot} $）和振动（$ t_ {vib} $）温度的快速光谱方法（$ t_ {rot} $），使用机器学习方法进行了激发态的两倍玻璃体分布。该方法应用于在碳膜材料的血浆增强化学蒸气沉积期间，在氢 - 甲烷气体混合物中的直流电流排放中估计分子气温。 $ c_2 $（$ν'= 0 \ t t t t t至ν''= 0 $）SWAN带系统的略有解析的RO振动光发射光谱用于等离子球的局部温度测量。探索了机器学习的随机森林算法，以确定温度分布图。除了$ t_ {rot} $，$ t_ {vib} $地图，分发地图及其电子温度的梯度（$ t_e $）以及光谱线516,5 $ nm $的发射强度，与$ C_2 $的物种相对应，并进行了详细讨论。
A fast optical spectroscopic method for determination rotational ($T_{rot}$) and vibrational ($T_{vib}$) temperatures in two-temperature Boltzmann distribution of the excited state by using machine learning approach is presented. The method is applied to estimate molecular gas temperatures in a direct current glow discharge in hydrogen-methane gas mixture during plasma-enhanced chemical vapor deposition of carbon film materials. Slightly resolved ro-vibrational optical emission spectrum of the $C_2$ ($ν'=0 \to ν''=0$) Swan band system was used for local temperature measurements in plasma ball. Random Forest algorithm of machine learning was explored for determination of temperature distribution maps. In addition to the $T_{rot}$ , $T_{vib}$ maps, distribution maps and their gradients for electron temperature ($T_e$) and for the emission intensity of the spectral line 516,5$nm$ corresponding to $C_2$ species is presented and is discussed in detail.
