The fabrication of semiconductor involves etching process to remove selected areas from wafers. However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines. Traditional image processing usually demands on large number of annotated data and the performance is still poor. We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer. Then, we use simple image processing to carry out automatic measurement on the objects. We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset. We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments. Our deep learning based method demonstrates superiority over image processing approach with  mean accuracy reaching over 96% for the measurements, compared with the ground truth. To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement. Typically, massive processing phases during fabrication of semiconductor can be roughly categorized into four manufacturing steps: deposition, removal, patterning, and modification. Particularly for etching process, as one type of removal technologies, such as Reactive-ion etching (RIE) BID6 , aims to remove selected areas from the surface of wafer so that other materials can be deposited BID12 . As the density of semiconductor device are continually increasing with miniaturization tendency in highly integrated circuits, features and size between etched structure also become dramatically smaller and leads to higher aspect ratio (means ratio of height to width), which in other words narrowing the horizontal width much faster than vertical height within the structure BID3 . Therefore, the precision during removal and patterning phases is quite crucial for semiconductor devices to ensure ultimate quality and reliability of products.However, how to guarantee the precision and accuracy during fabrication process remains a big challenge due to the following reasons: (1) Inaccurate measurement of critical dimensions, such as wrongly localized key points or failure of removing some material in etched process, can result in adverse consequence and impact on product yield and quality; (2) Even very slight deformation on patterns or features with irregular shape or bend line can cause some device defects, e.g., nonfunctionality or bad quality on next-generation products BID23 BID11 BID26 .Till now, although with the advent of high-resolution image using AFM (Atomic Force Microscopy) and advanced etched process like Deep Reactive-Ion Etching (DIRE) BID17 , the measurements, such as etch line, space depth, width, profile angle, etc., are still carried out manually by domain experts or process engineers. Such process requires considerable efforts and it is timeconsuming, subjective, and very hard to reproduce BID21 . Traditional approaches like thresholding BID24 and edge detecting BID7 are basically utilizing constraints on image intensity or object appearance which require relatively large training sets. Consequently , automatic measurement and profile characterization of etched structures in semiconductor is highly desirable for semiconductor industry to achieve consistent, efficient, and accurate evaluation of the device quality, and at the same time reduce the demand for human beings.With evolving of machine learning techniques, such as random forest BID22 , SVM BID8 ), AdaBoost (Lee et al., 2010 , etc., they can be widely applied in many different domains, such as clinical, object recognition, image segmentation BID15 , etc. This paper conducts preliminary study on segmentation of silicon SEM image by using traditional machine learning approaches. As results demonstrated in FIG0 , image analytics using the traditional methods are not so promising, which fail to detect several boundaries due to unknown variations in images. Another challenge lies in irregular shapes resulted from unexpected variations, as the traditional image processing searches the boundaries based on predefined patterns.Recently, explosion of interests is drawn on deep learning approaches and many state-of-the-art methods are leading the direction for automatic image segmentation and recognition in a broad fields, such as clinical like tumors detection BID2 , arts like music BID25 , nature language processing BID4 , to mention a few. However, till now deep learning approach has not been used in etched structure detection for semiconductor wafer images yet. Although the aforementioned deep learning network can achieve promising performance in segmentation and classification issues, it still suffers from learning abundant data without ground truth or obtaining labeled training dataset with expensive and considerable efforts. Therefore, data augmentation is crucial to achieve excellent results by training model with desired properties and invariance with limited dataset. To address such puzzle, BID9 designed Generative Adversarial networks (GAN) using adversarial process, which can be used to generate pseudo images to reduce demand for labeled data.In this paper, fully convolutional network, U-Net, is adopted to the new issue on profile characterization of etched structure in semiconductor SEM image. Problem from this domain is appropriate to select U-Net for object boundary detection as following reasons: Firstly, the dataset for neuronal structure segmentation BID19 and the SEM image used for etched structure segmentation are quite similar which are all in electron microscopic stacks. Next, replacing fully connected layers with convolutional layers can generate seamless segmentation results with smooth lines rather than jagged line BID14 . Third, the output images generated are with high resolution which resulting from replacement with upsampling operators, and this lays the foundation of measurement on key point sets and critical dimension of chips after segmentation. Furthermore, data augmentation is performed by using GAN to acquire pseudo images for training. Other approaches for increasing sample dataset involve cropping, contrast, flipping, etc. Finally, key point localization is performed to measure critical dimensions of etched structures in wafers.Concluded from the aforementioned literatures and studies, this paper has the following contributions:1. Unlike the traditional boundary searching based on image processing, deep learning treats the problem as a segmentation problem; 2. Many data augmentations have been explored, such as varying contrasts, flipping, rotation, adding salt-and-pepper, etc., in order to explore all potential variations;3. Cross-validation methods are used to avoid any potential data-leakage, e.g., if there are three images of the same type, two out of three are used as training dataset and the remaining one are used for testing; if there is only one image for a type, the image is cropped into three smaller ones and again, two out of the three are used as training and the remaining one is used for testing;4. GAN is also used to generate some data for training and around 0.5% improvement of pixel-wise accuracy on testing dataset has been observed.The rest of the paper is organized as following. In Section 2, some related work are discussed.In section 3, we present the Network architecture of deep learning approach. Section 4, training process and data augmentation are demonstrated . In section 5, experiments and results are further discussed. Finally , conclusion has been drawn in section 6. This paper has demonstrated deep learning methodes like fully convolutional network U-Net can be further applied in the new issue of etched structure segmentation even with very limited dataset, and have broad application prospects in semiconductor industry to replace the time-consuming manual measurements. Segmentation results using deep learning approach illustrate superiority over traditional machine learning method to solve the puzzle of undetected boundary and irregular shape problems. Although GAN is state-of-the-art deep learning technique, results in this paper indicate generic data augmentation methods are even more efficient and powerful to enlarge dataset, and can avoid additional manual labeling tasks. The superiority of transfer learning is not so apparent in our case due to diverse data types. In addition to high accuracy in profile characterization, fast prediction can also be guaranteed with testing time of less than 1 second on a single image.
Using deep learning method to carry out automatic measurement of SEM images in semiconductor industry