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

该基金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，并在整个晶圆计量和检测过程中扩展数字创新。这项研究将产生新的知识和原理，用于电子工业所需的晶圆/薄膜检测，计量，设计和制造。建模方法是为在晶圆尺度上的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，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和检测。该项目通过软硬件集成、互联互通、智能化、可视化和灵活自动化，加速半导体行业数字化转型。开发了半导体晶圆/薄膜测量和检测技术的集成智能框架，利用超分辨率3D成像技术和薄膜材料特性监测、诊断和控制晶圆级缺陷的质量。该基金支持半导体制造业劳动力的发展，为本科生和研究生（包括代表性不足的群体）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dexel深度学习算法的半导体过程自动化和数字化提供了晶圆/薄膜检测和计量能力，以检测晶圆级或封装级异常。频闪光谱功能与光谱成像技术相结合，可以在探针扫描晶圆表面时进行同步光谱分析和高速光谱响应和空间图像捕获。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，并预测晶圆等级、缺陷类型和缺陷位置。通过三维相关神经网络（CNN）和递归神经网络（RNN）架构，建立了基于dexel的三维晶圆形貌数据预测方法，提高了计算速度和预测精度。通过结合频闪光学和深度学习算法，本研究将填补自动化检测技术以及晶圆和薄膜异常和材料性能变化的基本识别方面的关键知识空白。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.