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: 2124999
• 负责人: ChaBum Lee
• 金额: $ 30.15万
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124999&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.

这笔赠款支持研究推进晶圆级半导体制造和检测技术，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扩展数字创新。这项研究将产生晶圆/检测过程中使用的新知识和原理。电子行业所需的薄膜检测、计量、设计和制造建模方法是为晶圆级各种缺陷类型的检测能力而创建的。半导体计量和检测工具目前是独立运行的独立机器。人们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和检测，该项目可以通过硬件和软件集成、连接性、智能化、可视化和灵活的自动化来加速半导体行业的数字化转型。开发半导体晶圆/薄膜计量和检测技术，通过使用超分辨率 3D 成像工艺以及薄膜材料特性来监控、诊断和控制晶圆级缺陷的质量。该资助支持半导体制造。劳动力发展，提供研究和为本科生和研究生（包括代表性不足的群体）提供教育机会，以获取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圆/薄膜检查和技术。检测晶圆级或封装级异常的计量能力频闪光谱能力与光谱成像技术相结合，可以对两者进行同步光谱分析和高速成像捕获。当探头扫描晶圆表面时，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圆等级、缺陷类型和 3D 晶圆的缺陷位置。通过结合 3D 相关神经网络 (CNN) 和循环神经网络 (RNN) 架构建立地形数据，以提高计算速度和预测精度。频闪光谱和深度学习算法，这项研究将填补自动检测技术以及晶圆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，并被认为是值得的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来获得支持。

项目成果

Wafer particle inspection technique using computer vision based on a color space transform model

• DOI: 10.1007/s00170-023-11888-y
• 发表时间: 2023-07
• 期刊: The International Journal of Advanced Manufacturing Technology
• 作者: Heebum Chun;Jingyan Wang;Jungsub Kim;Chabum Lee

A single camera unit-based three-dimensional surface imaging technique

• DOI: 10.1007/s00170-023-11866-4
• 发表时间: 2023-07
• 期刊: The International Journal of Advanced Manufacturing Technology
• 作者: Yinhe Wang;Xiangyu Guo;Jungsub Kim;Pengfei Lin;Kuan Lu;Hyunjae Lee;Chabum Lee