Table · dataset · 2026
Data for A Generalized Convolution-Based Framework for Quantitative SIMS Depth Profiling of Nanoscale Layers
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Nanoscale layers consist of ultra-sharp dopant distributions embedded within a matrix and are increasingly important for advanced electronic and quantum devices.
Description
Although an ideal $\delta$-layer is confined to a single atomic plane, real structures have a finite spatial extent owing to growth kinetics, interfacial roughness and atomic-scale intermixing. Quantitative characterisation therefore requires the intrinsic dopant distribution to be separated from the instrumental broadening.
In secondary ion mass spectrometry (SIMS), the measured profile is a convolution of the sample distribution with an energy-dependent response function arising from ion-beam-induced effects. Previous SIMS analyses have generally represented $\delta$-layers by either Gaussian or boxcar distributions, corresponding to two limiting profile shapes. Here, we introduce a convolution-based framework employing a flexible sample function that varies continuously between these limits.
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Combined with a global multi-energy fitting strategy, the approach separates the energy-dependent response function from the energy-independent sample profile and simultaneously determines their parameters. Application to representative boron $\delta$-layer structures produces consistent fits across a broad range of primary ion energies and reproducible intrinsic profiles from independently analysed layers. The framework provides a unified method for determining the intrinsic width and shape of nanoscale layers from energy-dependent SIMS measurements.<br><br>The data supporting the findings of this study are provided in the accompanying ZIP archives.
Figures 3 and S3 present a subset of the data obtained from a single BN δ-layer. The complete raw dataset corresponding to these figures is available in the archive B Si SingleLayer.zip. Likewise, Figures 4 and S4 present a subset of the data obtained from B δ-layers in Si.
The complete raw dataset corresponding to these figures is available in the archive B MultiLayer.zip.
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- DOI doi.org/10.82444/warw.33915784.v1 ↗
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Catalogue records · 1
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Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Mass spectrometry 75% · Nanoscale characterisation 80%
Provenance · 1 source records, 18 field assertions
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