Critical-Dimension Grazing-Incidence Small Angle X-Ray Scattering: Enhancing the latent signal using Bragg scattering
ORAL
Abstract
The semiconductor industry is continuously pushing the limits of photolithography, with feature sizes now under 10 nm. X-ray scattering1 has emerged as a possible contender to determine the average shape of a line grating with a sub-nanometer precision. However, to fulfill its promise, faster algorithms must also be developed to interpret and extract metrics from reciprocal space scattering data. We are presenting a novel, fast, and accurate X-ray technique and analysis algorithm: Critical Dimension Grazing Incidence X-ray Scattering, CD-GISAXS
The CD-GISAXS technique operates in grazing incidence configuration with a continuous azimuthal rotation of the sample, thus does not require high-energy X-rays to penetrate the wafer and greatly reduces the data acquisition times, permitting analysis within the framework of the DWBA. The Bragg rods coming from the line gratings, intersect with the momentum transfer vector of the elastic X-ray scattering at a single point above the horizon. The Bragg rods can be scanned by rotating the momentum transfer vector, and therefore the sample.
[1] D. Sunday et al, Journal of Micro/Nanolithography, MEMS, and MOEMS, 2013
The CD-GISAXS technique operates in grazing incidence configuration with a continuous azimuthal rotation of the sample, thus does not require high-energy X-rays to penetrate the wafer and greatly reduces the data acquisition times, permitting analysis within the framework of the DWBA. The Bragg rods coming from the line gratings, intersect with the momentum transfer vector of the elastic X-ray scattering at a single point above the horizon. The Bragg rods can be scanned by rotating the momentum transfer vector, and therefore the sample.
[1] D. Sunday et al, Journal of Micro/Nanolithography, MEMS, and MOEMS, 2013
*1. Center for Advanced Mathematics for Energy Research Applications
2. DOE Early Career Award to Alexander Hexemer
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Presenters
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Dinesh Kumar
- Computational Research, Lawrence Berkeley National Laboratory
- Advanced Light Source, Lawrence Berkeley National Laboratory