Nanoscale optical critical dimension measurement of a contact hole using deep ultraviolet spectroscopic ellipsometry

Chouaib, Houssam; Zhao, Qiang

doi:10.1116/1.4771969

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…It should be noted, however, that the proposed area measurement method does not account for the uncertainties due to the specific method of image generation (microscope, any type of camera, TEM, etc). As such, it is incumbent upon users of BHSAMA to consider their method of image generation and account for this appropriately in error propagation [18][19][20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Direct surface area measurement from digital images via brightness histogram method

Sendova¹,

Mancini²

2020

Meas. Sci. Technol.

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A direct, shape-and scale-independent digital image measurement method of the surface area (SA) of individual objects is proposed. The algorithms presented herein utilize the brightness histogram of 8-bit greyscale images, and thus are referred to as a brightness histogram surface area measurement algorithm (BHSAMA). The proposed SA measurement technique allows the uncertainty of the single measurement reading to be evaluated by traditional error propagation. Furthermore, the numerical value of the propagated uncertainty reflects the pixel brightness gradient of the edge pixels. This fact alone presents a significant advantage to the current methods utilizing image segmentation and/or edge detection whose measurement uncertainties cannot be quantitatively analysed. The proposed method does not involve any shape-related approximations. Five examples illustrating the method are discussed. For method verification purposes, a series of digital simulations using a control sample of predetermined size is undertaken. The accuracy of the single measured SA reading is between 0.5% and 1.5%. The uncertainty of the measured SA is evaluated and discussed further as a function of two types of simulated blurred edge region. The two presented BHSAMA techniques can have a wide range of applicability, from nanoparticles to cell biology to aerial imagery.

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Section: Discussionmentioning

confidence: 99%

Direct surface area measurement from digital images via brightness histogram method

Sendova¹,

Mancini²

2020

Meas. Sci. Technol.

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“…Figure 1. Two typical overlay process control loops: ADI metrology and ACI metrology Spectroscopic ellipsometry (SE) has been used for CD (critical dimension) metrology for many years as a robust overlay metrology solution due to the Mueller matrix signal from SE's sensitivity to overlay error metrology [1][2][3][4][5][6][7]. Furthermore, as an optical solution, SE offers the advantages of relatively high throughput and precision versus other overlay metrology solutions, such as CD-SEM.…”

Section: Introductionmentioning

confidence: 99%

Optical in die NZO benefit for DRAM manufacturing

Yeh,

Tsai,

Chu

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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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.

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“…This methodology combines a new principal component analysis (PCA) with rigorous coupled-wave analysis (RCWA) applied to the Mueller elements obtained through spectroscopy ellipsometry. Physics-based MM ellipsometry has already proven successful in a broad range of extremely complex structures, spanning from logic to memory applications [7][8][9] : it represents an accurate, robust, fast, and non-destructive method of characterizing the critical dimensions of interest. However, precision and accuracy of the measurements can often be hampered when the CDs are geometrically buried and/or unwanted correlations to other degrees of freedom obscure the useful spectral signal relative to the CD of interest.…”

Section: Introductionmentioning

confidence: 99%

Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors

Chouaib,

Dimastrodonato,

Chou

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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The semiconductor industry has witnessed a fast progression of spectroscopic ellipsometry (SE) techniques aimed at resolving a plethora of complex device characterizations on a nanometric scale. The Mueller Matrix (MM) methodology coupled with rigorous coupled-wave analysis (RCWA) has offered an unprecedented power of investigation and analysis of diverse critical dimensions (CDs), especially when applied to gate-all-around (GAA) structures, as it helps increase the useful spectral signals of the often geometrically buried CDs. However, the sensitivity to the CDs can be often screened by other parameters, hampering the precision and accuracy of the measurement. Combining the most sensitive MM elements has therefore become a critical step of scatterometry critical dimension (SCD) metrology. Driven by the rapid developments of Machine Learning (ML) algorithms, we propose a versatile ellipsometry methodology that overcomes poor sensitivity and increases accuracy through a novel principal component analysis (PCA) method of the ML training algorithm with RCWA assistance. Furthermore, our methodology introduces a new ML training concept based on reference data statistics, rather than raw reference. Our approach has been validated with reference data and proved successful in monitoring GAA sheet-specific indent. The proposed methodology paves the way to measuring low sensitivity CDs with highly accurate, noise-reduced and robust ML-based physical SCD models for any logic and memory application.

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Nanoscale optical critical dimension measurement of a contact hole using deep ultraviolet spectroscopic ellipsometry

Cited by 14 publications

References 11 publications

Direct surface area measurement from digital images via brightness histogram method

Direct surface area measurement from digital images via brightness histogram method

Optical in die NZO benefit for DRAM manufacturing

Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors

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