Proton Direct Ionization in Sub-Micron Technologies : Numerical Method for RPP Parameter Extraction
Lüdeke, S., & Javanainen, A. (2022). Proton Direct Ionization in Sub-Micron Technologies : Numerical Method for RPP Parameter Extraction. IEEE Transactions on Nuclear Science, 69(3), 254-263. https://doi.org/10.1109/tns.2022.3147592
Julkaistu sarjassa
IEEE Transactions on Nuclear SciencePäivämäärä
2022Tekijänoikeudet
© IEEE, 2022
This work introduces a numerical method to iteratively extract parameters of a rectangular parallelepiped (RPP) sensitive volume (SV) from experimental proton direct ionization SEU data. The method combines two separate numerical models. The first model estimates the average LET values for energetic ions, including protons and also heavy ions, in elemental solid targets. The second model describes the statistical variance in the energy deposition events of projectile-induced primary ionization within a RPP shaped target volume. To benchmark the method, simulated cross-section values based on RPP parameters derived with this method are compared with literature data from four SRAM devices. The RPP geometries determined by this method reproduced the experimental cross-section values in the literature with good accuracy, therefore showing that this method can be used to reliably and quickly determine the RPP parameters for SVs in memories sensitive to proton direct ionization (PDI). The method is currently strictly limited to direct ionization effects, i.e. not taking into account any nuclear reaction mechanisms, and elemental materials due to the underlying models’ definitions.
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Julkaisija
Institute of Electrical and Electronics Engineers (IEEE)ISSN Hae Julkaisufoorumista
0018-9499Asiasanat
Julkaisuun liittyvä(t) tutkimusaineisto(t)
Lüdeke, Sascha; Javanainen, Arto. (2021). Semi-empirical parameters for electronic stopping force model. Relative differences between experimental data and various stopping models (this model, SRIM2013, DPASS). V. 2.12.2021. University of Jyväskylä. https://doi.org/10.17011/jyx/dataset/78898. https://urn.fi/URN:NBN:fi:jyu-202112085888Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/104061542
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Lisätietoja rahoituksesta
This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement 721624, in part by Deutsche Forschungs-gemeinschaft (DFG) through its Major Research Instrumentation Programme under Grant INST 184/157-1 FUGG, and in part by the Ministry of Science and Culture (MWK) of the Lower Saxony State.Lisenssi
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