Collaborative Research: Data-Driven Metrology and Inspection Technology for Semiconductor Wafer-Level Manufacturing

合作研究：用于半导体晶圆级制造的数据驱动计量和检测技术

• 批准号: 2125826
• 负责人: Martin Jun
• 金额: $ 30万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125826&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Metrology

This grant supports research advancing wafer-level semiconductor manufacturing and inspection technology, establishing the data and technical architecture needed to ensure sustainable solutions and scaling digital innovation across the wafer metrology and inspection processes. This research will generate new knowledge and principles used in the wafer/thin-film inspection, metrology, design and manufacturing needed in the electronics industry. Modeling methodologies are created for the inspection capability of various defect types at wafer scale. Semiconductor metrology and inspection tools are presently stand-alone machines operated independently and there is an increasing need for creating an automated and integrated metrology and inspection across semiconductor manufacturing processes. This project can accelerate the semiconductor industry’s digital transformation through hardware and software integration, connectivity, intelligence, visualization, and flexible automation. An integrated and intelligent framework for semiconductor wafer/thin-film metrology and inspection technologies is developed to monitor, diagnose and control the quality of wafer-level defects, by using super-resolution 3D imaging process, as well as thin-film material properties. This grant supports the semiconductor manufacturing workforce development, providing research and education opportunities for undergraduate and graduate students including underrepresented groups to gain knowledge and hands-on experience in semiconductor technology. The semiconductor process automation and digitalization based on strobo-spectroscopy and dexel-based deep learning algorithms provide for a wafer/thin-film inspection and metrology capability to detect the wafer-level or packaging-level anomalies. A strobo-spectroscopy capability combined with a spectral imaging technology allows for the synchronized spectroscopic analysis and high-speed imaging capturing of both the spectral response and spatial images as the probe scans the wafer surface. The combined spectral response and camera images are converted to 3D data representations to train dexel-based deep learning algorithms and predict wafer grade, defect type, and defect locations. The dexel-based approach to 3D wafer topography data through 3D correlation Neural Network (CNN) and Recurrent Neural Network (RNN) architectures is established to improve computational speed and prediction accuracy. By combining strobo-spectroscopy and deep learning algorithms, this research will fill a critical knowledge gap in automated inspection technology and in the fundamental identification of the wafer and thin-film abnormalities and variation in material properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这笔赠款支持推进晶圆级半导体制造和检测技术的研究，建立确保可持续解决方案所需的数据和技术架构，并在晶圆计量和检测过程中推广数字创新。这项研究将产生用于电子工业所需的晶片/薄膜检测、计量、设计和制造的新知识和新原理。建立了晶圆尺度上各种缺陷类型的检测能力的建模方法。半导体计量和检验工具目前是独立运行的独立机器，对创建跨半导体制造工艺的自动化和集成的计量和检验的需求越来越大。该项目可以通过硬件和软件集成、连接、智能、可视化和灵活的自动化来加速半导体行业的数字化转型。针对半导体晶圆/薄膜计量检测技术的集成化、智能化框架，利用超分辨率3D成像技术和薄膜材料性能，对晶圆级缺陷的质量进行监测、诊断和控制。这笔赠款支持半导体制造劳动力的发展，为本科生和研究生(包括代表人数较少的群体)提供研究和教育机会，以获得半导体技术方面的知识和实践经验。基于频闪光谱和基于Dexel的深度学习算法的半导体工艺自动化和数字化提供了晶片/薄膜检测和计量能力，以检测晶片级别或封装级别的异常。频闪光谱功能与光谱成像技术相结合，允许在探测器扫描晶片表面时对光谱响应和空间图像进行同步光谱分析和高速成像捕获。组合的光谱响应和相机图像被转换为3D数据表示，以训练基于Dexel的深度学习算法，并预测晶片等级、缺陷类型和缺陷位置。为了提高计算速度和预测精度，利用三维相关神经网络(CNN)和递归神经网络(RNN)结构，建立了基于Dexel的三维硅片形貌数据处理方法。通过将频闪光谱和深度学习算法相结合，这项研究将填补自动检测技术以及晶片和薄膜异常和材料特性变化的基本识别方面的关键知识空白。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。