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Nondestructive, high-resolution, chemically specific 3D nanostructure characterization using phase-sensitive EUV imaging reflectometry

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Science Advances  27 Jan 2021:
Vol. 7, no. 5, eabd9667
DOI: 10.1126/sciadv.abd9667
  • Fig. 1 Experiment overview and nanostructure imaging.

    (A) Schematic of the amplitude- and phase-sensitive imaging reflectometer, which produces large-area, spatially and depth-resolved maps nondestructively. The incidence angle of the illumination is scanned by rotating the sample and detector in a θ-2θ configuration. The sample can also be scanned in 2D to perform ptychographic coherent diffractive imaging. Inset: Schematic representation of the imaged sample, which has SiO2 + Si3N4 structures patterned on As-doped regions with higher (~1%) and lower (~0.1%) peak dopant concentration. Native oxide layers (SiO2) are also present. (B and C) Zoom-in of EUV ptychographic phase reconstructions of the sample, (B) before and (C) after precise implementation of 3D tilted-plane correction and total variation (TV) regularization. (D) Entire, wide field-of-view amplitude reconstruction. Contours with corresponding labels on the right show the regions exposed to certain percentages of total photons that were incident on the sample during a single ptychography dataset. Small circles and corresponding labels on the left indicate the total number of photons that were incident on a pixel at that location over the duration of a single ptychography dataset (light incident at 30° from grazing). (E and F) Characteristic reflectivity versus angle curves for several bulk materials at 30-nm wavelength, showing the sensitivity of EUV light to material composition. The phase, measured by our reflectometer but not detected by others, can distinguish between materials even more sensitively than amplitude.

  • Fig. 2 Spatially resolved, composition-sensitive, 3D nanostructure characterization.

    Composition versus depth reconstruction in the (A) higher-doped structures, (B) lower-doped substrate, and (C) higher-doped substrate. The phase-sensitive imaging reflectometer has sensitivity to most parameters within this model (including layer thicknesses and the dopant concentration). Some parameters were determined by correlative imaging (such as surface roughness and interface diffusion). (D) Zoom-out and zoom-in (inset) of fully reconstructed sample. This combines the segmented high-fidelity ptychography reconstruction with the material reconstruction from the genetic algorithm, thus showing spatially and depth-resolved maps of material composition, doping, and topography. Different colors correspond to different materials. Notably, different regions of SiO2 are colored uniquely: patterned SiO2 under the structures, passivated SiO2 on higher- and lower-doped substrate, and passivated SiO2 on top of the structures. Also note that we reconstruct the etching adjacent to wide grating lines, shown in magenta in the inset.

  • Fig. 3 Correlative imaging with TEM and AFM.

    (A) High-angle annular dark-field (HAADF)–STEM image of one of the Si3N4 structures prepared by focused ion beam (FIB), with (B) a zoom-in showing the interfaces between Si, SiO2, and Si3N4. (C) An EDS image showing a different Si3N4 structure that is doped to ~1% on the right half and to ~0.1% on the left half. (D) EDS dopant-versus-depth profile that compares well to the curve obtained using SIMS measured on an unpatterned wafer. To increase the SNR, the energy-dispersive x-ray spectroscopy (EDS) profile was integrated over the area marked by the gray dotted box in (C). (E) Topography map obtained by combining the ptychographic phase image with the results of the genetic algorithm. The pixel size is 64 nm × 172 nm (vertical × horizontal), and the axial precision is 2 Å. (F) AFM image of the same region. Zoom-in on a region and averaged lineouts of that region are shown on the right.

  • Fig. 4 Correlation coefficients between the nine sample parameters in the composition-versus-depth reconstruction.

  • Table 1 Sensitivity of the phase-sensitive imaging reflectometer.

    This table compares the reconstructed values of different sample parameters by multiple metrology techniques. The “nominal value” column contains the design parameters. For phase-sensitive imaging reflectometry, the “Simultaneous” column shows the values simultaneously solved for using the genetic algorithm with the experimental data; only some of the sample parameters were solved for because of the limited number of data points. Were images at more angles available, we expect that we could simultaneously solve accurately for more of these parameters. The “single-parameter” column shows the sensitivity to these parameters in a single dimension, measured by how much the fit to the data worsens if an individual parameter is varied around the found solution. This column is a rough estimate of how low the confidence intervals could get with this dataset if we were solving for fewer parameters and were able to fix the rest using other metrology techniques. The error bars in the phase-sensitive imaging reflectometry columns are given at 1 SD, while the ranges reported for other techniques, when given, are more loosely defined reasonable ranges given to each measurement. For single-parameter confidence interval calculation, the dopant concentration versus depth was parameterized as the concatenation of an exponential spike at the surface and a Gaussian extending into the bulk (see the Supplementary Materials for complete table).

    FeatureNominal valuePhase-sensitive imaging
    Layer thickness
    SiO2 on Si3N4 structure0–4(Set to 3)± 0.33.0–5.0
    Si3N4 in structure50(Set to 50)Lower bound: 3041–45
    Patterned SiO2
    under structure
    5(Set to 5)No sensitivity at
    Structure height48.2 ± 0.2± 0.0245.0–45.848–51
    SiO2 on higher-
    doped substrate
    0–42.7 ± 0.3± <0.052.0–4.0
    SiO2 on lower-doped
    0–42.0 ± 0.3± <0.052.0–4.0
    Dopant-related etch
    6.09 ± 0.07± 0.027.8–8.05.5–7.5
    Interface quality
    Average surface/
    interface roughness
    (Set to 0.5)Upper bound: 0.80.5–1.0
    Surface roughness on
    (Set to 0.5)± 0.20.4–0.5
    Surface roughness on
    lower-doped substrate
    (Set to 0.5)± 0.10.4–0.5
    Surface roughness
    on higher-doped
    (Set to 0.5)± 0.30.4–0.5
    dose [atoms/cm2]
    1.10 × 10150.75 × 1015 Upper
    bound: 5.6 × 1015
    Upper bound:
    2.1 × 1015
    1.05 × 10151.30 × 1015
    Peak concentration
    [atomic %]
    (Shape set by
    Upper bound:–4.1
    Gaussian height
    [atomic %]
    (Shape set by
    Upper bound:–1.8
    Composition informationModel-basedSpectroscopicSpectroscopic
    Depth informationModel-basedDirectDirect
    Transverse spatial resolutionNano-scale (10–100 nm)TOF/nano-SIMS:
    ≥ 100 nm
    (10–100 nm)
    Atomic scale
    (1–100 Å)
    FOVMeso-to-micro (10–1000 μm)Meso (10 nm-
    100 μm)
    (1–1000 nm)
    Sample preparationMinimalMinimalMinimalVersatile

    * The dopant measurements by SIMS were taken on an unpatterned sister wafer. The technique could have made similar measurements on layer thicknesses if there were wafers with the same fabrication steps as this sample, but with much bigger feature sizes (size depends on instrument).

    † Variation in the SiO2 thicknesses between phase-sensitive imaging reflectometry (i.e., with phase and amplitude sensitivity) and EDS/HAADF is expected, because the sample had sufficient time to oxidize further between the two measurements. The sample was not prepared to perform surface roughness measurements.

    Supplementary Materials

    • Supplementary Materials

      Nondestructive, high-resolution, chemically specific 3D nanostructure characterization using phase-sensitive EUV imaging reflectometry

      Michael Tanksalvala, Christina L. Porter, Yuka Esashi, Bin Wang, Nicholas W. Jenkins, Zhe Zhang, Galen P. Miley, Joshua L. Knobloch, Brendan McBennett, Naoto Horiguchi, Sadegh Yazdi, Jihan Zhou, Matthew N. Jacobs, Charles S. Bevis, Robert M. Karl Jr., Peter Johnsen, David Ren, Laura Waller, Daniel E. Adams, Seth L. Cousin, Chen-Ting Liao, Jianwei Miao, Michael Gerrity, Henry C. Kapteyn, Margaret M. Murnane

      Download Supplement

      This PDF file includes:

      • Phase- and amplitude-sensitive imaging reflectometry: Theoretical sensitivity
      • Phase- and amplitude-sensitive imaging reflectometry: Numeric simulations
      • Future beamline optimization
      • Data collection at other incidence angles
      • Reconstruction challenges
      • Data preprocessing
      • Parallel ptychography code
      • Ptychography reconstruction
      • Phase-sensitive imaging reflectometry reconstructions
      • Image segmentation for producing reflectance versus angle curves
      • Composition reconstruction parameter selection
      • Reflectance calculation
      • Genetic algorithm used for composition reconstruction
      • Sensitivity of the phase-sensitive imaging reflectometer to more sample parameters
      • Figs. S1 to S12
      • Table S1
      • References

      Files in this Data Supplement:

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