Abstract
SRIM-like codes have limitations in describing general 3D geometries, for modeling radiation displacements and damage in nanostructured materials. A universal, computationally efficient and massively parallel 3D Monte Carlo code, IM3D, has been developed with excellent parallel scaling performance. IM3D is based on fast indexing of scattering integrals and the SRIM stopping power database, and allows the user a choice of Constructive Solid Geometry (CSG) or Finite Element Triangle Mesh (FETM) method for constructing 3D shapes and microstructures. For 2D films and multilayers, IM3D perfectly reproduces SRIM results, and can be ∼102 times faster in serial execution and > 104 times faster using parallel computation. For 3D problems, it provides a fast approach for analyzing the spatial distributions of primary displacements and defect generation under ion irradiation. Herein we also provide a detailed discussion of our open-source collision cascade physics engine, revealing the true meaning and limitations of the “Quick Kinchin-Pease” and “Full Cascades” options. The issues of femtosecond to picosecond timescales in defining displacement versus damage, the limitation of the displacements per atom (DPA) unit in quantifying radiation damage (such as inadequacy in quantifying degree of chemical mixing), are discussed.
Highlights
By comparing to standard reference values estimated by MD and NRT model, Stoller et al attributed this discrepancy between Full Cascades” (FC) and Quick Kinchin-Pease” (QKP) to a probable fundamental problem in the SRIM FC approach used to calculate the number of vacancies[44]
Because we have reproduced the SRIM results almost perfectly, it should be true that the SRIM FC values are significantly larger than QKP values because the FC option describes the off-site displacement pattern right after impact, at t = tI (~fs), while the QKP option estimates the damage at the refreezing point t = tF (~101 ps)
The FC method can be selected to reveal the inner workings of radiation damage and generate a more fine-grained picture of the radiation displacement pattern at t = tI (~fs), which are the natural initial conditions for the tI-to-tF evolver, whose outcome can feed into subsequent longer-term simulations like kinetic Monte Carlo (KMC) and cluster dynamics (CD)[82]
Summary
This discrepancy should come from the nano-energetics effect, including the difference of stopping powers and the displacement and binding energy thresholds between nanostructures and bulk. The nano-geometric effect is responsible for changes in the defect spatial distributions between different shapes (without taking into account the nano-energetics effect, which only becomes significant when the characteristics size scale drops to less than LC = 20 nm[83]) During plasma-surface interactions in PFMs86, these two effects (i.e., the bending and shading effects) should occur at the reconstructed surface, promoting the formation of “fuzz” nanostructures on the surface
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