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oai:figshare.com:article/33915784 2026-09-17T22:19:14Z portal_1197 item_type_3 month_year_09_2026

Data for A Generalized Convolution-Based Framework for Quantitative SIMS Depth Profiling of Nanoscale Layers Thomas P. A. Hase (21518034) Migrated from ePrints Nanoscale layers consist of ultra-sharp dopant distributions embedded within a matrix and are increasingly important for advanced electronic and quantum devices. 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. 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. 2026-09-17T22:19:14Z Dataset Dataset 10.82444/warw.33915784.v1 https://figshare.com/articles/dataset/Data_for_A_Generalized_Convolution-Based_Framework_for_Quantitative_SIMS_Depth_Profiling_of_Nanoscale_Layers/33915784 CC BY 4.0