Designing an anisotropic noise filter for measuring critical dimension and line edge roughness from scanning electron microscope images

Abstract

The scanning electron microscope (SEM) is often employed in inspecting patterns transferred through a lithographic process. A typical inspection is to measure the critical dimension (CD) and line edge roughness (LER) of each feature in a transferred pattern. Such inspection may be done by utilizing image processing techniques to detect the boundaries of a feature. Since SEM images tend to include a substantial level of noise, a proper reduction of noise is essential before the subsequent process of edge detection. In a previous study, a method of designing an isotropic Gaussian filter adaptive to the noise level was developed. However, its performance for relatively small features was not so good as for large features, especially in the case of LER. The main objective of this study is to improve the design method such that the accuracy of the measured CD and LER is not deteriorated substantially as the feature size decreases. The new design method allows a Gaussian filter to be anisotropic for the better adaptability to the signal and noise, both of which show a substantial level of directional correlation. The cutoff frequency for the direction normal to features is determined to include most of the signal components, and the cutoff frequency in the other direction is set to balance the signal and noise components to be included. This procedure enables a systematic and easy design of the filter. Also, the method of estimating the noise has been modified for higher accuracy. The performance of the new design method has been thoroughly analyzed using the reference images for which feature boundaries are known. It has been shown that the anisotropic filter designed by the proposed method performs better than the isotropic filter from the viewpoint of CD, LER, and power spectral density accuracy.

Surface Wettability Prediction Using Image Analysis and an Artificial Neural Network

“…The surface structure effect on wettability is multifactorial, so deterministic modeling of the relationship between the surface structure and wettability is difficult due to the unknown variables and measurability. The ANN is an ML technique; it is more effective than statistical techniques to define the non-linear relationship between the input (various roughness parameters) and the output (CA and ShA), therefore it is appropriate for this study . Digital-processing techniques can extract information about various surface structures from parameters computed from an image of the surfaces.…”

“…The ANN is an ML technique; it is more effective than statistical techniques to define the non-linear relationship between the input (various roughness parameters) and the output (CA and ShA), therefore it is appropriate for this study. 23 Digital-processing techniques can extract information about various surface structures from parameters computed from an image of the surfaces. The digital image-processing method has limitation that the image quality is not guaranteed to be consistent across samples or image-shooting conditions.…”

“…Some recent studies have suggested the use of machine-learning (ML) techniques to develop a model to predict wettability or to design a superhydrophobic surface structure. − A proposed superhydrophobic surface-design map that allows contact angles ≥150° and capillary pressures ≥845 Pa in micro-/submicro-sized surface structures was designed using an artificial neural network (ANN). ML methods can be used to efficiently predict the water wettability of metallic materials; the models provide better predictions of experimentally measured wettability than the traditional theoretical wetting models such as Wenzel or CB …”

“…Atomic force microscopy (AFM) analysis is commonly employed to quantify surface nanoroughness from differences in the heights of surface structures but cannot give structural information in detail from a horizontal perspective, such as spacing between structures or the shape of the structure plane. As a complementary method, some studies have tried to use digital image processing to quantify various surface structural information captured in scanning electron microscopy (SEM) images employing substrates with irregular surface structures. − The combination of image data analysis and ML methods increases the sophistication at which surface topographies can be evaluated and enables the prediction or control of roughness-related properties that cannot be described using a single parameter. , …”

“…18−20 A proposed superhydrophobic surface-design map 18 that allows contact angles ≥150°and capillary pressures ≥845 Pa in micro-/submicro-sized surface structures was designed using an artificial neural network (ANN). ML methods can be used to efficiently predict the water wettability of metallic materials; 23 the models provide better predictions of experimentally measured wettability than the traditional theoretical wetting models such as Wenzel or CB. 20 However, due to limitations of existing roughness evaluation methods, the surface morphological characteristics cannot be objectively quantified in a multifaceted way, for example, surface shape from a top point of view.…”