Fast forecasting of VGF crystal growth process by dynamic neural networks
Fast forecasting of VGF crystal growth process by dynamic neural networks
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DOI：
10.1016/j.jcrysgro.2019.05.022
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发表时间：
2019-09-01
影响因子：
1.8
通讯作者：
Winkler, Jan
中科院分区：
文献类型：
--
作者：
Dropka, Natasha;Holena, Martin;Winkler, Jan
晶体生长过程中工艺参数的快速预测是工艺开发、优化和控制的关键步骤。基于计算流体动力学建模的常用方法是准确的，但是太慢而不能真实的实时地提供结果。本文对动态人工神经网络在VGF-GaAs晶体生长冷却方案预测中的应用进行了可行性研究。特别是，我们研究了各种非线性自回归人工神经网络与外源输入（NARX）与2个外部输入和6个输出来自500瞬态数据集。通过瞬态1D CFD模拟生成数据。第一个令人鼓舞的结果，并提出了应用动态人工神经网络的VGF过程参数的快速预测的优点和缺点进行了讨论。
Fast forecasting of process variables during the crystal growth is a critical step in a process development, optimization and control. The common approach based on computational fluid dynamics modeling is accurate, but too slow to deliver results in real time. Here we conducted a feasibility study on the application of dynamic artificial neural networks in the forecasting of VGF-GaAs crystal growth cooling program. Particularly, we studied various Nonlinear-AutoRegressive artificial neural networks with eXogenous inputs (NARX) with 2 external inputs and 6 outputs derived from 500 transient data sets. Data were generated by transient 1D CFD simulation. The first encouraging results are presented and the pros and cons of the application of dynamic artificial neural networks for the fast predictions of VGF process parameters are discussed.