Computer simulation of electron thermalization in CsI and CsI(Tl)

Abstract

A Monte Carlo (MC) model was developed and implemented to simulate the thermalization of electrons in inorganic scintillator materials. The model incorporates electron scattering with both longitudinal optical and acoustic phonons. In this paper, the MC model was applied to simulate electron thermalization in CsI, both pure and doped with a range of thallium concentrations. The inclusion of internal electric fields was shown to increase the fraction of recombined electron-hole pairs and to broaden the thermalization distance and thermalization time distributions. The MC simulations indicate that electron thermalization, following γ-ray excitation, takes place within approximately 10 ps in CsI and that electrons can travel distances up to several hundreds of nanometers. Electron thermalization was studied for a range of incident γ-ray energies using electron-hole pair spatial distributions generated by the MC code NWEGRIM (NorthWest Electron and Gamma Ray Interaction in Matter). These simulations revealed that the partition of thermalized electrons between different species (e.g., recombined with self-trapped holes or trapped at thallium sites) vary with the incident energy. Implications for the phenomenon of nonlinearity in scintillator light yield are discussed.

Calculation of energy relaxation rates of fast particles by phonons in crystals

“…We have chosen a classical description of the particle to maintain compatibility with our scintillator simulations. [12][13][14]25,26 This approach is preferable because a wave-packet description of a particle in the Bloch picture requires the spatial extent of the region in space at which the particle might be measured to contain many unit cells (so that the packet can be limited to a relatively small region in k-space) while we want to model the interaction of the particle with point defects (e.g. activators) that are situated at certain definite positions.…”

“…A feature of the emerging understanding of these systems is that variations in the spatial density of secondary excitations lead to non-proportionality of the scintillation signal to the energy of the exciting radiation, which in turn limits the achievable energy resolution of radiation detectors. 1,[3][4][5]7,11 Detailed modeling based on Monte-Carlo methods, [12][13][14] pursued by the present authors and their collaborators, is shedding light on the microscopic processes occurring in scintillators. In this approach, an attempt is made to parametrize all the microscopic physical processes (e.g.…”

Simulating Interface Growth and Defect Generation in CZT – Simulation State of the Art and Known Gaps

“…Therefore, it is necessary to construct a model of the microstructural evolution during crystal growth in order to fully understand to coupling of observed microstructures to growth conditions. With the aid of a microstructural response model of gamma-ray interactions, such as that provided by NWEGRIM [4][5][6], this type of approach would allow growth optimization of CZT to achieve improved gamma-ray energy resolution.…”

“…Many methods of improving the level of approximation of the many electron Schrödinger equation have been proposed (e.g., MP2, or CCSD(T) or Quantum Monte Carlo methods) and been shown to provide sufficient accuracy. These approaches are extremely computationally intensive (e.g., the method considered to be most accurate for ground state calculations, MP2, scales as N 5 , where N is the number of electrons). Today QMC calculations are likely feasible for a subset of native defects, and in the near future MP2 level calculations should be come feasible for up to 100 atoms.…”

“…A general approach for generating a response model has been developed at PNNL and SNL that consists of using developed interatomic potentials together with ab initio methods to develop accurate structural models of extended defects in CZT and to determine their effects on charge generation and transport [4][5][6]. The approach employs a Monte Carlo method for tracking the generation and transport of individual electrons and holes The Monte Carlo method uses several input parameters, which can be obtained from ab initio calculations.…”