Automated transmission electron microscopy for defect review and metrology of Si devices

Strauss, M.; Williamson, Mark

doi:10.1109/asmc.2013.6552761

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…In this study, we present an automated TEM metrology and EDS characterization workflow for plan view DRAM capacitors. [9][10][11][12] We utilized a new X-ray Energy-Dispersive Spectroscopy (EDS) detector namely Ultra-X detector for elemental analysis. The Ultra-X detector provides high collection efficiency, enhancing throughput and accuracy in EDS metrology characterization.…”

Section: Introductionmentioning

confidence: 99%

Automated TEM metrology and EDS characterization of plan view DRAM capacitors

Gnanasekaran,

Strauss,

Zhou

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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Capacitors play an essential role in dynamic random-access memory (DRAM) devices. With continuous DRAM device scaling, critical dimension measurements and elemental analysis of capacitor structures becomes more critical. Here, we present an automated TEM metrology and EDS characterization workflow for plan view DRAM capacitors. We utilized a Metrios 6 (Scanning) Transmission Electron Microscope ((S)TEM) equipped with an Ultra-X Energy Dispersive X-Ray Spectroscopy (EDS) detector to obtain high-quality analytical information of DRAM capacitors. The large solid angle of the Ultra-X detector provides approximately 3-4 times more X-ray counts than the previous generation Dual-X detector. Further, we show Ultra-X detector can characterize the elemental composition even on layers as thin as ~1 nm using low electron doses. Additionally, we demonstrate that the extended EDS acquisition induces elemental diffusion and structural alterations, leading to a ~4% reduction in the radius of certain DRAM layers. These findings underscore the importance of controlling the electron dose during imaging to ensure accurate, reliable, and reproducible CD measurements.

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Section: Introductionmentioning

confidence: 99%

Automated TEM metrology and EDS characterization of plan view DRAM capacitors

Gnanasekaran,

Strauss,

Zhou

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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“…In this study, we present (S)TEM metrology characterization [5][6][7][8][9] using an automated Thermo Scientific TM Metrios TM (S)TEM on two typical process challenges. We introduce an internal machine learning-based modeling algorithm to address the challenge of measuring GAA structures with sacrificial SiGe etch process variations using a forksheet device.…”

Section: Introductionmentioning

confidence: 99%

Automated (S)TEM metrology characterization of gate-all-around and 3D NAND devices

Strauss

Hakala

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Fab and defect inspection workflows have reached an inflection point with the introduction of (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 (). 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 devices by an automated MetriosTM TEM. We introduce an internal -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 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 .

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“…In the case of the former, researchers are typically forced to choose between accepting the control limitations set by manufacturers (often necessary to guarantee performance specifications) or designing a bespoke instrument. The community has developed multiple innovative approaches to high-throughput screening and automation (Carragher et al, 2000;Mastronarde, 2003;Schorb et al, 2019), most prominently in the fields of cell biology (Coudray et al, 2011;Yin et al, 2020), medical diagnostics (Gurcan et al, 2009;Martin-Isla et al, 2020), single-particle cryo electron microscopy (Liu et al, 2016;Frank, 2017), but also in crystallography (Rauch & Véron, 2014), semiconductor metrology (Strauss & Williamson, 2013), and particle analysis (House et al, 2017;Uusimaeki et al, 2019). More recently, manufacturers have begun to provide increased access to low-level instrument functions (Meyer et al, 2019;Kalinin et al, 2021;PyJEM).…”

Section: Introductionmentioning

confidence: 99%

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Olszta

Hopkins

Fiedler

et al. 2022

Microscopy and Microanalysis

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Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

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Automated transmission electron microscopy for defect review and metrology of Si devices

Cited by 7 publications

References 5 publications

Automated TEM metrology and EDS characterization of plan view DRAM capacitors

Automated TEM metrology and EDS characterization of plan view DRAM capacitors

Automated (S)TEM metrology characterization of gate-all-around and 3D NAND devices

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

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