Multiscale Modeling of Silicon Heterojunction Solar Cells

abstract: Silicon photonic technology continues to dominate the solar industry driven by steady improvement in device and module efficiencies. Currently, the world record conversion efficiency (~26.6%) for single junction silicon solar cell technologies is held by silicon heterojunction (SHJ) solar cells based on hydrogenated amorphous silicon (a-Si:H) and crystalline silicon (c-Si). These solar cells utilize the concept of carrier selective contacts to improve device efficiencies. A carrier selective contact is designed to optimize the collection of majority carriers while blocking the collection of minority carriers. In the case of SHJ cells, a thin intrinsic a-Si:H layer provides crucial passivation between doped a-Si:H and the c-Si absorber that is required to create a high efficiency cell. There has been much debate regarding the role of the intrinsic a-Si:H passivation layer on the transport of photogenerated carriers, and its role in optimizing device performance. In this work, a multiscale model is presented which utilizes different simulation methodologies to study interfacial transport across the intrinsic a-Si:H/c-Si heterointerface and through the a-Si:H passivation layer. In particular, an ensemble Monte Carlo simulator was developed to study high field behavior of photogenerated carriers at the intrinsic a-Si:H/c-Si heterointerface, a kinetic Monte Carlo program was used to study transport of photogenerated carriers across the intrinsic a-Si:H passivation layer, and a drift-diffusion model was developed to model the behavior in the quasi-neutral regions of the solar cell. This work reports de-coupled and self-consistent simulations to fully understand the role and effect of transport across the a-Si:H passivation layer in silicon heterojunction solar cells, and relates this to overall solar cell device performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Geometric Algorithms and Data Structures for Simulating Diffusion Limited Reactions

Radiation therapy is one of the most effective means for treating cancers. An important calculation in radiation therapy is the estimation of dose distribution in the treated patient, which is key to determining the treatment outcome and potential side effects of the therapy. Biological dose — the level of biological damage (e.g., cell killing ratio, DNA damage, etc.) inflicted by the radiation is the best measure of treatment quality, but it is very difficult to calculate. Therefore, most clinics today use physical dose - the energy deposited by incident radiation per unit body mass - for planning radiation therapy, which can be calculated accurately using kinetic Monte Carlo simulations. Studies have found that physical dose correlates with biological dose, but exhibits a very complex relationship that is not yet well understood. Generally speaking, the calculation of biological dose involves four steps: (1) the calculation of physical dose distribution, (2) the generation of radiochemicals based on the physical dose distribution, (3) the simulation of interactions between radiochemicals and bio-matter in the body, and (4) the estimation of biological damage based on the distribution of radiochemicals. This dissertation focuses on the development of a more efficient and effective simulation algorithm to speed up step (3). The main contribution of this research is the development of an efficient and effective kinetic Monte Carlo (KMC) algorithm for simulating diffusion-limited chemical reactions in the context of radiation therapy. The central problem studied is - given n particles distributed among a small number of particle species, all allowed to diffuse and chemically react according to a small number of chemical reaction equations - predict the radiochemical yield over time. The algorithm presented makes use of a sparse grid structure, with one grid per species per radiochemical reactant used to group particles in a way that makes the nearest neighbor search efficient, where particles are stored only once, yet are represented in grids of all appropriate reaction radii. A kinetic data structure is used as the time stepping mechanism, which provides spatially local updates to the simulation at a frequency which captures all events - retaining accuracy. A serial and three parallel versions of the algorithm have been developed. The parallel versions implement the kinetic data structure using both a standard priority queue and a treap data structure in order to investigate the algorithms scalability. The treap provides a way for each thread of execution to do more work in a particular region of space. A comparison with a spatial discretization variant of the algorithm is also provided

An adaptive Cartesian embedded boundary approach for fluid simulations of two- and three-dimensional low temperature plasma filaments in complex geometries

We review a scalable two- and three-dimensional computer code for low-temperature plasma simulations in multi-material complex geometries. Our approach is based on embedded boundary (EB) finite volume discretizations of the minimal fluid-plasma model on adaptive Cartesian grids, extended to also account for charging of insulating surfaces. We discuss the spatial and temporal discretization methods, and show that the resulting overall method is second order convergent, monotone, and conservative (for smooth solutions). Weak scalability with parallel efficiencies over 70\% are demonstrated up to 8192 cores and more than one billion cells. We then demonstrate the use of adaptive mesh refinement in multiple two- and three-dimensional simulation examples at modest cores counts. The examples include two-dimensional simulations of surface streamers along insulators with surface roughness; fully three-dimensional simulations of filaments in experimentally realizable pin-plane geometries, and three-dimensional simulations of positive plasma discharges in multi-material complex geometries. The largest computational example uses up to $800$ million mesh cells with billions of unknowns on $4096$ computing cores. Our use of computer-aided design (CAD) and constructive solid geometry (CSG) combined with capabilities for parallel computing offers possibilities for performing three-dimensional transient plasma-fluid simulations, also in multi-material complex geometries at moderate pressures and comparatively large scale.Comment: 40 pages, 21 figure

Efficient Reactive Brownian Dynamics

We develop a Split Reactive Brownian Dynamics (SRBD) algorithm for particle simulations of reaction-diffusion systems based on the Doi or volume reactivity model, in which pairs of particles react with a specified Poisson rate if they are closer than a chosen reactive distance. In our Doi model, we ensure that the microscopic reaction rules for various association and disassociation reactions are consistent with detailed balance (time reversibility) at thermodynamic equilibrium. The SRBD algorithm uses Strang splitting in time to separate reaction and diffusion, and solves both the diffusion-only and reaction-only subproblems exactly, even at high packing densities. To efficiently process reactions without uncontrolled approximations, SRBD employs an event-driven algorithm that processes reactions in a time-ordered sequence over the duration of the time step. A grid of cells with size larger than all of the reactive distances is used to schedule and process the reactions, but unlike traditional grid-based methods such as Reaction-Diffusion Master Equation (RDME) algorithms, the results of SRBD are statistically independent of the size of the grid used to accelerate the processing of reactions. We use the SRBD algorithm to compute the effective macroscopic reaction rate for both reaction- and diffusion-limited irreversible association in three dimensions. We also study long-time tails in the time correlation functions for reversible association at thermodynamic equilibrium. Finally, we compare different particle and continuum methods on a model exhibiting a Turing-like instability and pattern formation. We find that for models in which particles diffuse off lattice, such as the Doi model, reactions lead to a spurious enhancement of the effective diffusion coefficients.Comment: To appear in J. Chem. Phy

Ion beam processing of surfaces and interfaces – Modeling and atomistic simulations

Self-organization of regular surface pattern under ion beam erosion was described in detail by Navez in 1962. Several years later in 1986 Bradley and Harper (BH) published the first self-consistent theory on this phenomenon based on the competition of surface roughening described by Sigmund’s sputter theory and surface smoothing by Mullins-Herring diffusion. Many papers that followed BH theory introduced other processes responsible for the surface patterning e.g. viscous flow, redeposition, phase separation, preferential sputtering, etc. The present understanding is still not sufficient to specify the dominant driving forces responsible for self-organization. 3D atomistic simulations can improve the understanding by reproducing the pattern formation with the detailed microscopic description of the driving forces. 2D simulations published so far can contribute to this understanding only partially. A novel program package for 3D atomistic simulations called trider (TRansport of Ions in matter with DEfect Relaxation), which unifies full collision cascade simulation with atomistic relaxation processes, has been developed. The collision cascades are provided by simulations based on the Binary Collision Approximation, and the relaxation processes are simulated with the 3D lattice kinetic Monte-Carlo method. This allows, without any phenomenological model, a full 3D atomistic description on experimental spatiotemporal scales. Recently discussed new mechanisms of surface patterning like ballistic mass drift or the dependence of the local morphology on sputtering yield are inherently included in our atomistic approach. The atomistic 3D simulations do not depend so much on experimental assumptions like reported 2D simulations or continuum theories. The 3D computer experiments can even be considered as ’cleanest’ possible experiments for checking continuum theories. This work aims mainly at the methodology of a novel atomistic approach, showing that: (i) In general, sputtering is not the dominant driving force responsible for the ripple formation. Processes like bulk and surface defect kinetics dominate the surface morphology evolution. Only at grazing incidence the sputtering has been found to be a direct cause of the ripple formation. Bradley and Harper theory fails in explaining the ripple dynamics because it is based on the second-order-effect ‘sputtering’. However, taking into account the new mechanisms, a ‘Bradley-Harper equation’ with redefined parameters can be derived, which describes pattern formation satisfactorily. (ii) Kinetics of (bulk) defects has been revealed as the dominating driving force of pattern formation. Constantly created defects within the collision cascade, are responsible for local surface topography fluctuation and cause surface mass currents. The mass currents smooth the surface at normal and close to normal ion incidence angles, while ripples appear first at θ ≥ 40°. The evolution of bimetallic interfaces under ion irradiation is another application of trider described in this thesis. The collisional mixing is in competition with diffusion and phase separation. The irradiation with He+ ions is studied for two extreme cases of bimetals: (i) Irradiation of interfaces formed by immiscible elements, here Al and Pb. Ballistic interface mixing is accompanied by phase separation. Al and Pb nanoclusters show a self-ordering (banding) parallel to the interface. (ii) Irradiation of interfaces by intermetallics forming species, here Pt and Co. Well-ordered layers of phases of intermetallics appear in the sequence Pt/Pt3Co/PtCo/PtCo3/Co. The trider program package has been proven to be an appropriate technique providing a complete picture of mixing mechanisms

Kinetic Monte-Carlo modelling of charge and exciton dynamics in phosphorescent organic light-emitting diodes

Stephen Sanderson studied the operation of phosphorescent organic light-emitting diodes using kinetic Monte-Carlo simulation techniques combined with molecular dynamics modelling for accurate representations of the molecular structure. His research has contributed to an improved understanding of the underlying physical processes in these devices