论文标题
机器学习和原子层沉积：使用人工神经网络预测反应堆生长曲线的饱和时间
Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks
论文作者
论文摘要
在这项工作中，我们探讨了基于在ALD反应器的不同点上获得的厚度值，深度神经网络在原子层沉积过程的优化中的应用。我们引入了一个旨在训练神经网络的数据集，以根据单个实验条件在反应堆的不同点上测得的剂量时间和厚度值来预测饱和时间。然后，我们探索不同的人工神经网络配置，包括深度（隐藏层的数量）和大小（每个层中的神经元数），以更好地了解神经网络必须达到高预测精度的大小和复杂性。获得的结果表明，受过训练的神经网络可以准确预测饱和时间，而无需任何先前有关表面动力学的信息。这提供了一种可行的方法，可以最大程度地减少优化已知反应器中新ALD过程所需的实验数量。但是，数据集和训练过程取决于反应器几何形状。
In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at different points of an ALD reactor. We introduce a dataset designed to train neural networks to predict saturation times based on the dose time and thickness values measured at different points of the reactor for a single experimental condition. We then explore different artificial neural network configurations, including depth (number of hidden layers) and size (number of neurons in each layers) to better understand the size and complexity that neural networks should have to achieve high predictive accuracy. The results obtained show that trained neural networks can accurately predict saturation times without requiring any prior information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor. However, the datasets and training procedure depend on the reactor geometry.
