Poster + Paper
9 April 2024 Optical in die NZO benefit for DRAM manufacturing
Author Affiliations +
Conference Poster
Abstract
Today, with the accelerating complexity of nanoelectronics for memory applications, in-die overlay metrology has required much tighter control. A typical in-device overlay control strategy utilizes high-voltage SEM metrology across several key layers, but lot and wafer sampling is limited due to low system throughput. Our objective is to find a faster, more robust, and more efficient optical metrology solution that can produce the same in-die overlay results vs. SEM. In this work, we create a novel solution using the KLA SpectraShape 11k dimensional metrology system to demonstrate improved nonzero overlay (NZO) control that meets the tighter overlay budget requirement. We combined the spectroscopic Mueller matrix of SpectraShape 11k and the machine learning algorithm of TurboShape modeling software. Both real spectra collected by SpectraShape 11k and theoretical spectra generated from the scatterometry model are trained against their corresponding SEM reference and synthetic reference data respectively to predict the overlay value. Accurate and robust optical in-device overlay results are proven with a high correlation to the HV-SEM data. In addition, the SpectraShape 11k in-device overlay is equipped with a few key performance indicators (KPIs) including CIndex and CD profile, which are designed to flag process excursions in an HVM environment. Good agreement is observed between the KPIs and overlay delta to HV-SEM. Finally, the 4x-8x throughput advantage of optical metrology in-device overlay vs. SEM in-device overlay allows users to set more dense wafer measurements by lot or dense site measurements by wafer, enabling better lot-to-lot or wafer-to-wafer NZO control.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Fang-Jyun (June) Yeh, Shang-Hong (Sam) Tsai, Hsiao Lun Chu, Kai Lee, Yun-Ju (York) Yang, Houssam Chouaib, and Zhengquan Tan "Optical in die NZO benefit for DRAM manufacturing", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129552B (9 April 2024); https://doi.org/10.1117/12.3009956
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KEYWORDS
Overlay metrology

Semiconducting wafers

Machine learning

Scanning electron microscopy

Education and training

Critical dimension metrology

High volume manufacturing

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