Characteristics of accuracy for CD metrology

Banke, G. W.; Archie, Charles N.

doi:10.1117/12.350818

Cited by 23 publications

(3 citation statements)

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“…Figure 1 shows a schematic diagram of the reference measurement concept. Other examples of reference measurement systems include work by Banke and Archie [9], and Marchman [10].…”

Section: Introductionmentioning

confidence: 99%

Higher order tip effects in traceable CD-AFM-based linewidth measurements

Orji

Dixson

2007

Meas. Sci. Technol.

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In critical dimension atomic force microscopy (CD-AFM), a major source of uncertainty is due to the tip. Measurements made using a CD-AFM tip show an apparent broadening of the feature width. Usually, the linewidth can be approximately corrected if the tip width is known. In addition to tip width broadening, tip-shape-dependent effects—also known as higher order tip effects—are a contributor to the uncertainty of CD-AFM linewidth measurements. These are especially important for undercut features and samples with relatively large sidewall roughness. In this paper, we examine the different types of CD-AFM higher order tip effects within the context of a reference measurement system and present a procedure for estimating some of these contributions with an uncertainty of less than 1.5 nm.

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“…Figure 1 shows a schematic diagram of the reference measurement concept. Other examples of reference measurement systems include work by Banke and Archie [9], and Marchman [10].…”

Section: Introductionmentioning

confidence: 99%

Higher order tip effects in traceable CD-AFM-based linewidth measurements

Orji

Dixson

2007

Meas. Sci. Technol.

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“…This facilitates the CD-AFM measurements postresist shrink due to more space available to the CD-AFM probe to properly scan the trench. 8 TMU analysis provided the systematic average offset and the relative accuracy between the extrapolated CDs and the CD-AFM preshrink. At this point, it is important to state that the CD-SEM measurement is not the physical measurement for two reasons (1) CD-SEM shrinks the resist during the first measurement and the shrink is anisotropic depending on the pattern geometry and dimensions, (2) the threshold settings in the CD-SEM measurement algorithm do not necessarily measure at the secondary electron signal or its derivative at a location corresponding to the CD of interest such as bottom CD in our case.…”

Section: Function Fitting Methodsmentioning

confidence: 99%

Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes

Rana

Zhang

Kagalwala

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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Integrated circuit (IC) technology is changing in multiple ways: 193i to extreme ultraviolet exposure, planar to nonplanar device architecture, from single exposure lithography to multiple exposure and directed selfassembly (DSA) patterning, and so on. Critical dimension (CD) control requirement is becoming stringent and more exhaustive: CD and process windows are shrinking, three-sigma CD control of <2 nm is required in complex geometries, and a metrology uncertainty of <0.2 nm is required to achieve the target CD control for advanced IC nodes (e.g., 14, 10, and 7 nm nodes). There are fundamental capability and accuracy limits in all the metrology techniques that are detrimental to the success of advanced IC nodes. Reference or physical CD metrology is provided by atomic force microscopy (CD-AFM) and TEM while workhorse metrology is provided by CD-SEM, scatterometry, and model-based infrared reflectrometry (MBIR). Precision alone is not sufficient for moving forward. No single technique is sufficient to ensure the required accuracy of patterning. The accuracy of CD-AFM is ∼1 nm and the precision in TEM is poor due to limited statistics. CD scanning electron microscopy (CD-SEM), scatterometry, and MBIR need to be calibrated by reference measurements for ensuring the accuracy of patterned CDs and patterning models. There is a dire need for a measurement with <0.5 nm accuracy and the industry currently does not have that capability with inline measurements. Being aware of the capability gaps for various metrology techniques, we have employed data processing techniques and predictive data analytics, along with patterning simulation and metrology models and data integration techniques to selected applications demonstrating the potential solution and practicality of such an approach to enhance CD metrology accuracy. Data from multiple metrology techniques have been analyzed in multiple ways to extract information with associated uncertainties and integrated to extract the useful and more accurate CD and profile information of the structures. This paper presents the optimization of scatterometry and MBIR model calibration and the feasibility to extrapolate not only in design and process space but also from one process step to a previous process step. A well-calibrated scatterometry model or patterning simulation model can be used to accurately extrapolate and interpolate in the design and process space for lithography patterning where AFM is not capable of accurately measuring sub-40 nm trenches. The uncertainty associated with extrapolation can be large and needs to be minimized. We have made use of measurements from CD-SEM and CD-AFM, along with the patterning and scatterometry simulation models to estimate the uncertainty associated with extrapolation and the methods to reduce it. For the first time, we have reported the application of machine learning (artificial neural networks) to the resist shrinkage systematic phenomenon to accurately predict the preshrink CD based on supervised learning using the CD-AFM data. ...

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“…However, to perform a precise accuracy assessment it should be assumed that both variables (xi, yi) in a regression process, have an amount of uncertainty. This leads to consider a Mandel regression instead of an ordinary least square regression and synthetize the results of the Mandel regression through a total measurement uncertainty (TMU) analysis [26][27]22].…”

Section: Metricsmentioning

confidence: 99%

Can remote SEM contours be used to match various SEM tools in fabs?

Pradelles

Perraud

Sezestre

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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CD-SEM is one of the workhorse metrology tools for evaluating the patterning performances in the semiconductor industry. With the continuous shrink of device feature sizes, measurement uncertainty, of such devices should improve accordingly, including tool-to-tool matching. Great efforts are made in fabs to ensure matching between CD-SEMs. Basically, there are two approaches : tool matching based on statistical CD measurements and tool matching based on physical tool parameters. The first methods require long recipe run on each tool for data collection while the second methods require dedicated maintenance tool time. In parallel, the emergence of SEM contour metrology brought new opportunities, for example in OPC modeling, process assessment or overlay metrology. Recently, a step has been made to demonstrate that offline SEM contour metrology can be matched with standard CD-SEM. In this paper we evaluate the possibility to use SEM contour metrology to perform a remote CD matching on images coming from two different CD-SEM. A large set of patterns of various dimensions and shapes (both 1D and 2D) is imaged with two different CD-SEM after etch process step. For each image the contours are extracted using a remote software. A matching model is calibrated to match contour measurements of the same pattern imaged with the two CD-SEMs. The matching model can be calibrated using either one or both inline SEM measurement or using additional metrology data (typically AFM measurement or any reference metrology measurement). The matching model parameters include both parameters for the contour extraction calibration and measurement matching parameters. Two modeling approaches are compared : one matching model averaging multiple measurement matching sites and a multiple (per pattern type) contour calibrations/ measurement matching sites model. These different matching models are assessed using as main metrics the remaining CD difference between SEM contour measurements on images coming from the two CD-SEM (ΔCDmatching) and TMU analysis. We show that an overall ΔCDmatching variability of 0.6 nm and a TMU below 2 nm can be obtained with the dataset. We evaluated a strategy to achieve matching of two CD-SEM using a remote contour extraction solution and a matching model.

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Characteristics of accuracy for CD metrology

Cited by 23 publications

References 0 publications

Higher order tip effects in traceable CD-AFM-based linewidth measurements

Higher order tip effects in traceable CD-AFM-based linewidth measurements

Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes

Can remote SEM contours be used to match various SEM tools in fabs?

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