Fab metrology and defect inspection workflows have reached an inflection point with the introduction of gate-all-around (GAA) and high aspect ratio memory structures. Few existing inspection and metrology tools can match the sub-surface imaging and analytical capability provided by (Scanning) Transmission Electron Microscopy ((S)TEM). Fully automated (S)TEM workflows are becoming a necessity for the industry to deliver high volume metrology reference data. This atomic scale data must be available with fast turnaround and must also be statistically valid to speed up learning cycles. In this study, we present (S)TEM metrology characterization of advanced GAA and 3D NAND devices by an automated MetriosTM TEM. We introduce an internal machine learning-based modeling algorithm to address the challenges of recognizing GAA devices with process variations and provide faster access to highly accurate TEM reference metrology data. We present automated EDS characterization of beam-sensitive ONO layers, which is a key challenge in 3D NAND device metrology, enabled by a new generation of EDS detector with a high collection efficiency. We also present results on (S)TEM metrology during process monitoring of GAA devices with a higher level of TEM automation.
The scaling of device dimensions has resulted in a need for high resolution metrology techniques capable of measuring small CDs with a high degree of precision and accuracy. Scanning transmission electron microscopy (STEM) has previously been demonstrated to be a metrology technique capable of measuring small CDs and gathering large volumes of accurate and precise metrology data. In addition, energy dispersive X-ray spectroscopy (EDS) metrology has also been demonstrated to be a powerful technique enabling the detection and measurement of low contrast layers, specifically for 3D NAND devices. Benchmarking EDS metrology against STEM metrology in terms of precision and accuracy is important to further investigate the capabilities of EDS metrology for the semiconductor industry. This study was performed using the latest technology in EDS detectors, along with automated acquisition and metrology software to generate large metrology data sets on horizontal nanowire structures. In this paper, we present data to support our finding that EDS metrology is well-matched with STEM metrology in terms of both precision and accuracy. In addition, we discuss the capability of EDS and STEM metrology to detect subtle process variations in next-generation logic devices.
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