Sixteen-megabit dynamic random access memory trench depth characterization using two-dimensional diffraction analysis

Hatab, Ziad; McNeil, J. R.; Naqvi, S. Sohail H.

doi:10.1116/1.587994

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…Another category of popular new applications are shape acquisition methods, where the screen is used as a planar illuminant, while the camera captures the illuminated scene. For example, methods using a screen-camera setup have been invented for the purpose of photometric stereo [5,12,11], specular surface acquisition [9,23,27], environment matting [29], and many more applications [17,22,19]. The attractive feature that all these techniques have in common, is the fact that a cheap setup consisting only of off-the-shelf components can often replace otherwise expensive hardware setups, while still producing comparable results.…”

Section: Introductionmentioning

confidence: 99%

Screen-Camera Calibration Using Gray Codes

Francken

Hermans

Bekaert

2009

2009 Canadian Conference on Computer and Robot Vision

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In this paper we present a method for efficient calibration of a screen-camera setup, in which the camera is not directly facing the screen. A spherical mirror is used to make the screen visible to the camera. Using Gray code illumination patterns, we can uniquely identify the reflection of each screen pixel on the imaged spherical mirror. This allows us to compute a large set of 2D-3D correspondences, using only two sphere locations. Compared to previous work, this means we require less manual interventions, combined with a more robust screen pixel detection scheme. This results in a consistent improvement in accuracy, which we illustrate with experiments on both synthetic and real data.

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Section: Introductionmentioning

confidence: 99%

Screen-Camera Calibration Using Gray Codes

Francken

Hermans

Bekaert

2009

2009 Canadian Conference on Computer and Robot Vision

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“…Now, the goal is to find an assignment between the x and the y in the form of regression coefficients These coefficients express how much information from each regressor x will be necessary to complete the correlation to one regressand Because ofthe (linear) approximation character ofthe operation, an additional error term e has to be introduced. This may be vritten as: yi,j = c0, + c1,x1,1 ÷ c2,•x,2 + +cp,j.xi,p + (2) With mean centering for removing the -tenms and using a matrix notation, this can be rewitten to: Y=1C+E (3) where C has the dimensions p,h and E is of the same size as Y (n,h).…”

Section: Basic Principle and Linear Methodsmentioning

confidence: 99%

<title>Photoresist metrology based on light scattering</title>

Bischoff

Baumgart

Truckenbrodt

et al. 1996

Metrology, Inspection, and Process Control for Microlithography X

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Angle resolved light scatteromeiry along with advanced data analysis is a promising new metrology technique to meet the challenges oftoday's and tomorrow's submicron technology. The measurement accuracy strongly depends on the performance capabilities of the algorithms utilized for data exploration and analysis. Presently, multivariate regression methods such as inverse least squares and principal component approaches are prefened. Substantial accuracy gains may be achieved by applying quasi-nonlinear methods, i.e. nonlinear data pretreatment followed by the usual linear regression. In this way, not only were the linewidth prediction errors in measuring developed resist lines pushed to below 20 nm, but likewise more complicated tasks as silylation profile evaluation and latent image measurement could be addressed satisfactorily. . INTRODUCTIONRecently, a new method was introduced in micrometrology, which consists in a combination of angle resolved scattered light measurement (ARS) and sophisticated data analysis '. A high measurement throughput, sufficient accuracy in the submicron range, nondesiructive probing and on-line capability are the crucial benefits ofthis metrology. The basic principle ofthe light measurement is straightforward: A focused laser beam is impinging on the surface under investigation. Caused by the diffraction and the microroughness ofthe sample, the light is scattered into the full hemisphere above the specimen. Considering only onedimensional surfaces (e.g. line space patterns) and adjusting the incident beam perpendicular to the lines, the light distribution will essentially be confmed to the plane of incidence. The resulting light distribution strongly depends on the topographic and optical properties ofthe sample. Thus, it may be regarded as a kind of "optical fingerprint". Particularly, ifa periodic line/space pattern is illuminated with a laser spot covering more than a few pitches, sharp diffraction orders stand out from the noisy background. Now, the difficulty remains of identifying the profile ofthe scatterer from this "fingerprint". Due to the ambiguity between scattering surface and measured light intensity distribution, an analytical solution ofthe problem may not be gained in general. However, if certain a-priori information about the surface profile and the refraction indices is given, a quantitative assignment between light intensities and profile parameters such as linewidth, height, slope angle and others may be obtained by establishing multivariate regression models. Until now, several applications such as DRAM trench depth characterization 2, latent image and post exposure bake monitoring , PSM meirology and submicron linewidth resist metrology based on scatterometry were published. Predominantly, linear regression models including the inverse least squares method (ILS), the principal component regression (PCR) and the partial least squares method (PLS) known from chemometrics 6 are applied. Owing to the linearization inherent in these models, the resulting accuracy is ...

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“…Due to the variety of possibilities for illumination and detection there are many approaches to the forward problem. Although several types of scatterometers have been investigated over the years [10][11][12][13] , it is the angular, or 2-Θ variety that was used in this study. In this arrangement, the incident angle is varied in order to obtain a scatter signature.…”

Section: Introductionmentioning

confidence: 99%

Metal etcher qualification using angular scatterometry

et al. 2005

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The deployment of angular scatterometry as a powerful and effective process control methodology has recently included the measurement of etched metal features in a typical complex Aluminum stack. With the control of metal process steps taking a more critical role in silicon manufacturing, a fast, reproducible and accurate methodology for measuring CD and depth is necessary. With the half-pitch of the metal pattern being as low as the minimum device feature, etch rate measurements on above-micron test structures are hardly indicative of the pattern-dependent etch profiles and behavior. Angular scatterometry offers a non-destructive, fast and powerful approach for measuring the profiles of the yield-relevant array features in metal applications.In this work we demonstrate the application of angular scatterometry to the qualification of metal etchers. Etch depth is difficult to control and must be inspected with slow techniques such as profilometry. In addition to the slow response time and sparse radial sampling, contact profilometry is susceptible to residual resist and polymer residue as well as to the variations in the TiN ARC layer affecting the measurement of the Aluminum etch rates. We show that the choice of a suitable profile model and accurate knowledge of the optical properties allow scatterometry to overcome all of these traditional challenges.We demonstrate that angular scatterometry is sensitive to the parameters of interest for controlling metal etchers, specifically etch depth, CD and profile. Across an experimental design that introduced intentional variations in these parameters, angular scatterometry results were able to track the variations accurately. In addition, profile results determined through scatterometry compare favorably with cross-sectional SEM images and measurements. Measurement precision results will also be presented.

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Sixteen-megabit dynamic random access memory trench depth characterization using two-dimensional diffraction analysis

Abstract: Toggle magnetic random access memory cells scalable to a capacity of over 100 megabits

Cited by 18 publications

References 13 publications

Screen-Camera Calibration Using Gray Codes

Screen-Camera Calibration Using Gray Codes

<title>Photoresist metrology based on light scattering</title>

Metal etcher qualification using angular scatterometry

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