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.

该赠款支持研究推进动力级半导体制造和检查技术的研究，建立了确保可持续解决方案并扩展整个WAVER METRROLOGY和检查过程所需的数据和技术体系结构。这项研究将产生在电子行业所需的Waver/薄膜检查，计量，设计和制造中使用的新知识和原理。创建建模方法是为了在晶状体尺度上以各种缺陷类型的检查能力进行检查。半导体计量和检查工具是独立操作的独立机器，并且越来越需要在半导体制造过程中创建自动化和集成的计量学和检查。该项目可以通过硬件和软件集成，连接性，智能，可视化和灵活的自动化来加速半导体行业的数字转换。通过使用超分辨率3D成像过程以及薄膜材料属性，开发了用于半导体晶圆/薄膜计量和检查技术的集成且智能的框架。该赠款支持半导体制造业劳动力发展，为包括代表性不足的团体在内的本科生和研究生提供研究和教育机会，以获得半导体技术方面的知识和动手经验。基于Strobo-SpectRoscopicy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摇摆/薄膜检查和计量能力，以检测摇动级别或包装级别的异常。随着探测器扫描摇动表面时，频谱成像技术与光谱成像技术结合使用，可以进行光谱分析和高速成像捕获。组合的光谱响应和摄像头图像转换为3D数据表示，以训练基于Dexel的深度学习算法并预测晶圆级，缺陷类型和缺陷位置。通过3D相关神经网络（CNN）和复发神经网络（RNN）架构建立了基于Dexel的3D Waver地形数据的方法，以提高计算速度和预测准确性。通过将频谱光谱和深度学习算法结合在一起，这项研究将填补自动检查技术的关键知识差距，以及对Waver和薄膜异常和材料属性的基本识别的基本识别以及材料属性的变化。该奖项反映了NSF的法定任务，并通过评估了基金会的评估，并通过评估了基金会和广泛的支持。