Development of Overlay Target's Centre Positioning Algorithms Using Customizable Shape Fitting for High-Precision Wafer Bonding

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rui Wang, Yixian Zhu, Sen Lu, Kaiming Yang, Yu Zhu
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引用次数: 0

Abstract

Wafer bonding is a critical process in 3D integration, and overlay (OVL) metrology is essential for its success. Accurately positioning the centre of OVL targets is fundamental for effective metrology. However, the identification and localization of target centres become challenging due to complex shapes and unexpected features, such as rounded corners, that can arise during manufacturing. An algorithm is proposed to tackle this challenge by employing customizable shape fitting. This method begins with the extraction of sub-pixel edge points, followed by applying a Hough transform to group and smooth these points, thereby enhancing contour quality. By parameterizing the target shape based on specific points, the algorithm integrates sub-pixel traversal techniques with an optimization objective, achieving sub-pixel accuracy in centre positioning. Simulation results indicate that the algorithm can achieve a positioning accuracy of ±0.03 pixels and demonstrates robustness against noise and blur. Finally, the proposed algorithm was used to test the OVL target pair arrays fabricated by electron beam etching, confirming an accuracy of ±0.04 pixels (±6.9 nm). These results validate the algorithm's capability to meet high precision requirements for OVL target centre positioning in wafer applications.

Abstract Image

基于可定制形状拟合的高精度晶圆键合覆盖目标中心定位算法的发展
晶圆键合是三维集成的关键工艺,而OVL测量是其成功的关键。准确定位OVL目标的中心是有效测量的基础。然而,由于在制造过程中可能出现复杂的形状和意想不到的特征(如圆角),目标中心的识别和定位变得具有挑战性。提出了一种采用可定制形状拟合的算法来解决这一问题。该方法首先提取亚像素边缘点,然后应用霍夫变换对这些点进行分组和平滑,从而提高轮廓质量。该算法通过基于特定点参数化目标形状,将亚像素遍历技术与优化目标相结合,实现了亚像素精度的中心定位。仿真结果表明,该算法的定位精度为±0.03像素,对噪声和模糊具有较强的鲁棒性。最后,将该算法应用于电子束刻蚀制备的OVL靶对阵列,精度为±0.04像素(±6.9 nm)。这些结果验证了该算法能够满足晶圆应用中OVL目标中心定位的高精度要求。
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来源期刊
IET Image Processing
IET Image Processing 工程技术-工程:电子与电气
CiteScore
5.40
自引率
8.70%
发文量
282
审稿时长
6 months
期刊介绍: The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications. Principal topics include: Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality. Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing. Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing. Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video. Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography. Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security. Current Special Issue Call for Papers: Evolutionary Computation for Image Processing - https://digital-library.theiet.org/files/IET_IPR_CFP_EC.pdf AI-Powered 3D Vision - https://digital-library.theiet.org/files/IET_IPR_CFP_AIPV.pdf Multidisciplinary advancement of Imaging Technologies: From Medical Diagnostics and Genomics to Cognitive Machine Vision, and Artificial Intelligence - https://digital-library.theiet.org/files/IET_IPR_CFP_IST.pdf Deep Learning for 3D Reconstruction - https://digital-library.theiet.org/files/IET_IPR_CFP_DLR.pdf
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