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

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.