利用机器视觉定量薄膜中的缺陷
Quantifying Defects in Thin Films using Machine Vision
摘要：
薄膜材料和器件对缺陷的敏感性促使人们对薄膜形态的优化进行广泛的研究。这项研究可以加速自动化实验，表征薄膜形态的合成条件的响应。光学成像可以解决薄膜中的形态缺陷，并且容易集成到自动化实验中，但是由这样的系统产生的大量图像需要自动化分析。现有的自动分析光学图像中的膜形态的方法需要软件专家进行特定于应用的定制，并且对图像内容或成像条件的变化不鲁棒。在这里，我们提出了一个通用的卷积神经网络（CNN）的薄膜图像分析，可以识别和量化的各种缺陷的程度，并适用于多种材料和成像条件。这种CNN很容易适应新的薄膜图像分析任务，并将促进自动化薄膜研究系统中的成像的使用。
英文摘要：
The sensitivity of thin-film materials and devices to defects motivates extensive research into the optimization of film morphology. This research could be accelerated by automated experiments that characterize the response of film morphology to synthesis conditions. Optical imaging can resolve morphological defects in thin films and is readily integrated into automated experiments but the large volumes of images produced by such systems require automated analysis. Existing approaches to automatically analyzing film morphologies in optical images require application-specific customization by software experts and are not robust to changes in image content or imaging conditions. Here we present a versatile convolutional neural network (CNN) for thin-film image analysis which can identify and quantify the extent of a variety of defects and is applicable to multiple materials and imaging conditions. This CNN is readily adapted to new thin-film image analysis tasks and will facilitate the use of imaging in automated thin-film research systems.
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