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Register a new userDesign, development and validation of electron ionisation models for nano-scale simulation
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H. Seo
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Two theory-driven models of electron ionization cross sections, the Binary-Encounter-Bethe model and the Deutsch-Mark model, have been design and implemented; they are intended to extend the simulation capabilities of the Geant4 toolkit. The resulting values, along with the cross sections included in the EEDL data library, have been compared to an extensive set of experimental data, covering more than 50 elements over the whole periodic table.
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Two theory-driven models of electron ionization cross sections, the Binary-Encounter-Bethe model and the Deutsch-Mark model, have been design and implemented; they are intended to extend the simulation capabilities of the Geant4 toolkit. The resulting values, along with the cross sections included in the EEDL data library, have been compared to an extensive set of experimental data, covering more than 50 elements over the whole periodic table.
Backscattering is a sensitive probe of the accuracy of electron scattering algorithms implemented in Monte Carlo codes. The capability of the Geant4 toolkit to describe realistically the fraction of electrons backscattered from a target volume is extensively and quantitatively evaluated in comparison with experimental data retrieved from the literature. The validation test covers the energy range between approximately 100 eV and 20 MeV, and concerns a wide set of target elements. Multiple and single electron scattering models implemented in Geant4, as well as preassembled selections of physics models distributed within Geant4, are analyzed with statistical methods. The evaluations concern Gean
Several models for the Monte Carlo simulation of Compton scattering on electrons are quantitatively evaluated with respect to a large collection of experimental data retrieved from the literature. Some of these models are currently implemented in general purpose Monte Carlo systems; some have been implemented and evaluated for possible use in Monte Carlo particle transport for the first time in this study. Here we present first and preliminary results concerning total and differential Compton scattering cross sections.
Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these data-driven compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuits behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.
The high performance requirements at the European Spallation Source have been driving the technological advances on the neutron detector front. Now more than ever is it important to optimize the design of detectors and instruments, to fully exploit the ESS source brilliance. Most of the simulation tools the neutron scattering community has at their disposal target the instrument optimization until the sample position, with little focus on detectors. The ESS Detector Group has extended the capabilities of existing detector simulation tools to bridge this gap. An extensive software framework has been developed, enabling efficient and collaborative developments of required simulations and analyses -- based on the use of the Geant4 Monte Carlo toolkit, but with extended physics capabilities where relevant (like for Bragg diffraction of thermal neutrons in crystals). Furthermore, the MCPL (Monte Carlo Particle Lists) particle data exchange file format, currently supported for the primary Monte Carlo tools of the community (McStas, Geant4 and MCNP), facilitates the integration of detector simulations with existing simulations of instruments using these software packages. These means offer a powerful set of tools to tailor the detector and instrument design to the instrument application.
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