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.

中文翻译：

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使用深度学习对固体表面离子轰击引起的图案形成进行参数估计

通过斜入射宽束离子轰击固体表面产生的纳米结构通常由各向异性 Kuramoto-Sivashinsky 方程建模。该方程有五个参数，每个参数取决于目标材料和离子种类、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个图像来估计运动方程中的所有五个参数，均方根误差低于用于训练的参数范围的 3%。这提供了一种工具，允许实验人员快速确定给定溅射实验的参数。它还可以独立检查其他估计参数的方法，例如结合火山口函数形式主义的原子模拟。