Loew Kevin M, Bradley R Mark
Department of Physics, Colorado State University, Fort Collins, CO 80523, United States of America.
Departments of Physics and Mathematics, Colorado State University, Fort Collins, CO 80523, United States of America.
J Phys Condens Matter. 2021 Jan 13;33(2):025901. doi: 10.1088/1361-648X/abb996.
The nanostructures produced by oblique-incidence broad beam ion bombardment of a solid surface are usually modelled by the anisotropic Kuramoto-Sivashinsky equation. This equation has five parameters, each of which depend on the target material and the ion species, energy, and angle of incidence. We have developed a deep learning model that uses a single image of the surface to estimate all five parameters in the equation of motion with root-mean-square errors that are under 3% of the parameter ranges used for training. This provides a tool that will allow experimentalists to quickly ascertain the parameters for a given sputtering experiment. It could also provide an independent check on other methods of estimating parameters such as atomistic simulations combined with the crater function formalism.
通过对固体表面进行斜入射宽束离子轰击产生的纳米结构通常由各向异性的Kuramoto-Sivashinsky方程建模。该方程有五个参数，每个参数都取决于靶材以及离子种类、能量和入射角。我们开发了一种深度学习模型，该模型使用表面的单张图像来估计运动方程中的所有五个参数，其均方根误差在用于训练的参数范围的3%以内。这提供了一种工具，使实验人员能够快速确定给定溅射实验的参数。它还可以对其他估计参数的方法进行独立验证，比如结合坑函数形式的原子模拟。
