A holistic metrology approach: hybrid metrology utilizing scatterometry, CD-AFM, and CD-SEM

Vaid, Alok; Yan, Bin Bin; Jiang, Yun Tao; Kelling, Mark; Hartig, Carsten; Allgair, John A.; Ebersbach, Peter; Sendelbach, Matthew; Rana, Narender; Katnani, Ahmad D.; Mclellan, Erin; Archie, Charles N.; Bozdog, Cornel; Kim, Helen; Sendler, Michael; Ng, Susan; Sherman, B.; Brill, Boaz; Turovets, Igor; Urensky, Ronen

doi:10.1117/12.881632

Cited by 43 publications

(26 citation statements)

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“…A similar situation has been encountered in other areas of dimensional nano-metrology and especially in the CD metrology of semiconductor structures, where recently the idea of holistic or hybrid metrology has been proposed and elaborated [13][14]. According to it, the results of two or more measuring methods are combined in a synergetic manner to exploit the merits of each method and minimize the impact of their limitations.…”

Section: Introductionmentioning

confidence: 99%

Model-aided hybrid metrology for surface roughness measurement fusing AFM and SEM data

Constantoudis

Boulousis

Ellinas

et al. 2015

17th International Congress of Metrology

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In this paper, we propose a hybrid metrology approach to the measurement and characterization of the nanoroughness of freeform film surfaces. The basic idea is to combine measurement data from Atomic Force and Scanning Electron Microscopes (AFM and SEM respectively) in a synergetic manner exploiting the advantages of both methods and reducing the effects of their shortcomings. The fusion of data is realized through a model reconstruction of the measured rough surface. In particular, the hybrid approach is implemented by obtaining the height distribution function from AFM topographies (given the high accuracy in AFM height measurements) and the autocorrelation function or Fourier transform of the surface morphology from SEM images (given the high spatial resolution in SEM images). These functions are then used as input to an algorithm to generate rough surfaces in which AFM tip size effects are minimized and hence are more accurate statistical representation of the real surface. The output morphologies may be employed to estimate all roughness parameters and metrics and especially the so-called hybrid parameters (active surface area, distributions of surface derivatives and curvatures etc.) which depend on both vertical and spatial roughness aspects. The latter parameters may be critical in many applications where surface roughness is used to control wetting behaviour, light scattering, bioadhesion or wear properties. As an example, we apply the hybrid approach to the estimation of the active surface area of a sample of cyclic olefin film etched in oxygen plasma for wetting control.

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Section: Introductionmentioning

confidence: 99%

Model-aided hybrid metrology for surface roughness measurement fusing AFM and SEM data

Constantoudis

Boulousis

Ellinas

et al. 2015

17th International Congress of Metrology

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“…In such a context, we propose an approach to build a taxonomic classification of nanoparticle and nanomaterial shapes based on quantitative measures of geometric features following recent developments in shape metrology. 24 Our approach bears in mind the future complementarity with other classification efforts.…”

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confidence: 99%

Towards the taxonomic categorization and recognition of nanoparticle shapes

Muñoz-Mármol

Crespo

Fritts

et al. 2015

Nanomedicine: Nanotechnology, Biology and Medicine

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“…This PDF is usually called the prior distribution π pri . In the case of a normally distributed random vector p it is given by: (5) By subtracting the prior distribution, or more precisely its logarithm, from the function in Eq. 3, the negative log-likelihood, we get a function that is proportional to the negative logarithm of the posterior probability distribution.…”

Section: Bayesian Approachmentioning

confidence: 99%

“…Since the term hybrid metrology has gained increased significance in the dimensional metrology community outside NIST [5][6][7] we will start this work with a short overview of two of the most common techniques in Section 2, namely the Bayesian approach and combined regression. The measured targets and the generalized parameter sets that describe them are discussed in Section 3 before we give a detailed description of the performed error analysis both for OCD and SEM in Section 4 and their impact on the hybridization in Section 5.…”

Section: Introductionmentioning

confidence: 99%

Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression

et al. 2015

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Hybrid metrology, e.g., the combination of several measurement techniques to determine critical dimensions, is an increasingly important approach to meet the needs of the semiconductor industry. A proper use of hybrid metrology may yield not only more reliable estimates for the quantitative characterization of 3-D structures but also a more realistic estimation of the corresponding uncertainties. Recent developments at the National Institute of Standards and Technology (NIST) feature the combination of optical critical dimension (OCD) measurements and scanning electron microscope (SEM) results. The hybrid methodology offers the potential to make measurements of essential 3-D attributes that may not be otherwise feasible. However, combining techniques gives rise to essential challenges in error analysis and comparing results from different instrument models, especially the effect of systematic and highly correlated errors in the measurement on the χ 2 function that is minimized. Both hypothetical examples and measurement data are used to illustrate solutions to these challenges.

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A holistic metrology approach: hybrid metrology utilizing scatterometry, CD-AFM, and CD-SEM

Cited by 43 publications

References 0 publications

Model-aided hybrid metrology for surface roughness measurement fusing AFM and SEM data

Model-aided hybrid metrology for surface roughness measurement fusing AFM and SEM data

Towards the taxonomic categorization and recognition of nanoparticle shapes

Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression

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