Quantum Monte Carlo study of silicon self-interstitial defects

We give a brief description of the variational and diffusion quantum Monte Carlo methods and their application to the study of self-interstitial defects in silicon. The diffusion quantum Monte Carlo calculations give formation energies for the most stable defects of about 4.9 eV, which is considerably larger than the values obtained in density functional theory methods. The quantum Monte Carlo results indicate a value for the formation+migration energy of the self-interstitial contribution to self-diffusion of about 5 eV, which is consistent with the experimental data.Peer Reviewe

Accuracy of Quantum Monte Carlo Methods for Point Defects in Solids

Quantum Monte Carlo approaches such as the diffusion Monte Carlo (DMC) method are among the most accurate many-body methods for extended systems. Their scaling makes them well suited for defect calculations in solids. We review the various approximations needed for DMC calculations of solids and the results of previous DMC calculations for point defects in solids. Finally, we present estimates of how approximations affect the accuracy of calculations for self-interstitial formation energies in silicon and predict DMC values of 4.4(1), 5.1(1) and 4.7(1) eV for the X, T and H interstitial defects, respectively, in a 16(+1)-atom supercell

Phase transformation in Si from semiconducting diamond to metallic beta-Sn phase in QMC and DFT under hydrostatic and anisotropic stress

Silicon undergoes a phase transition from the semiconducting diamond phase to the metallic beta-Sn phase under pressure. We use quantum Monte Carlo calculations to predict the transformation pressure and compare the results to density functional calculations employing the LDA, PBE, PW91, WC, AM05, PBEsol and HSE06 exchange-correlation functionals. Diffusion Monte Carlo predicts a transition pressure of 14.0 +- 1.0 GPa slightly above the experimentally observed transition pressure range of 11.3 to 12.6 GPa. The HSE06 hybrid functional predicts a transition pressure of 12.4 GPa in excellent agreement with experiments. Exchange-correlation functionals using the local-density approximation and generalized-gradient approximations result in transition pressures ranging from 3.5 to 10.0 GPa, well below the experimental values. The transition pressure is sensitive to stress anisotropy. Anisotropy in the stress along any of the cubic axes of the diamond phase of silicon lowers the equilibrium transition pressure and may explain the discrepancy between the various experimental values as well as the small overestimate of the quantum Monte Carlo transition pressure

Modeling and Simulation of Compositional Engineering in Sige Films Using Patterned Stress Fields

Semiconductor alloys such as silicon-germanium (SiGe) offer attractive environments for engineering quantum-confined structures that are the basis for a host of current and future optoelectronic devices. Although vertical stacking of such structures is routinely achieved via heteroepitaxy, lateral manipulation has proven much more challenging. I describe a new approach that suggests that a patterned elastic stress field generated with an array of nanoscale indenters in an initially compositionally uniform SiGe substrate will drive atomic interdiffusion, leading to compositional patterns in the near-surface region of the substrate. While this approach may offer a potentially efficient and robust pathway to producing laterally ordered arrays of quantum-confined structures, there is a large set of parameters important to the process. Thus, it is difficult to consider this approach using only costly experiments, which necessitates detailed computational analysis. First, I review computational approaches to simulating the long length and time scales required for this process, and I develop and present a mesoscopic model based on coarse-grained lattice kinetic Monte Carlo that quantitatively describes the atomic interdiffusion processes in SiGe alloy film subjected to applied stress. I show that the model provides predictions that are quantitatively consistent with experimental measurements, and I examine the impact of basic indenter geometries on the patterning process. Second, I extend the model to investigate the impact of several process parameters, such as more complicated indenter shapes and pitches. I find that certain indenter configurations produce compositional patterns that are favorable for use as lateral arrays of quantum-confined structures. Finally, I measure a set of important physical parameters, the so-called “activation volumes” that describes the impact of stress on diffusion. The values of these parameters are not well established in the literature. I make quantitative connections to the range of values found in the literature and characterize the effects of different stress states on the overall patterning process. Finally, I conclude with ideas about alternative pathways to quantum confined structure generation and possible extensions of the framework developed

A fourfold coordinated point defect in silicon

Due to their technological importance, point defects in silicon are among the best studied physical systems. The experimental examination of point defects buried in bulk is difficult and evidence for the various defects usually indirect. Simulations of defects in silicon have been performed at various levels of sophistication ranging from fast force fields to accurate density functional calculations. The generally accepted viewpoint from all these studies is that vacancies and self interstitials are the basic point defects in silicon. We challenge this point of view by presenting density functional calculations that show that there is a new fourfold coordinated point defect in silicon that is lower in energy

Atomistic Simulations of Ge on Amorphous Silica Substrates

High-quality Ge substrates have numerous applications, including high-efficiency III-V multijunction solar cells and photodetectors. But the high cost of single-crystalline Ge makes the use of Ge-on-Si virtual substrates more practical for device fabrication. However, the lattice mismatch between Ge and Si leads to a highly strained Ge layer when grown directly on the Si lattice. The high mismatch strain unavoidably leads to defects, primarily dislocations, that degrade the Ge film quality. Several approaches for mitigating these defects have been proposed, including selective epitaxial growth (SEG), in which one employs an amorphous layer (most often SiO2) as a mask to reduce the epitaxial contact between the Ge and Si lattices to lower the mismatch strain. SEG has been demonstrated to successfully produce high-quality Ge films on Si, although defects are not fully eliminated. Further improvements will require quantitative understanding of the underlying atomic-scale mechanisms. In this work, we present a computational framework to atomistically model the components of the SEG system (Si/SiO2/Ge). The model is validated by comparing predictions to experimental observations and ab initio calculations of various properties related to crystalline Si and Ge and amorphous SiO2, as well as combinations of these materials. The framework is then applied to study in detail the deposition of Ge on amorphous SiO2. It is shown that the simulations are able to access experimentally meaningful deposition conditions and reproduce several quantities related to the island size distribution. We then extend our simulation framework for deposition to include coarse projective integration (CPI). CPI is a multiscale modeling technique well-suited for situations, like atomic deposition, in which a system exhibits fast, stochastic processes, superposed onto slowly-evolving dynamics. In particular, we demonstrate an approach for generating atomistic configurations from limited knowledge of an island size distribution, which represents one of the key challenges in applying CPI to atomistic deposition. The results generated here should be easily adaptable to other deposition systems

Electronic Structure of SiC/SiO2 by Density Functional Theory

Silicon carbide (SiC) is a promising semiconductor material with desirable properties for many applications. SiC-based electronic devices and circuits are being developed for use in high-temperature, high-power, and high-radiation conditions under which conventional semiconductors cannot function. Additionally, it has the advantage of growing a native oxide, SiO2, by simple thermal oxidation. Despite all desirable properties, SiC-based devices still face major challenges. The main problem of SiC-based devices is the great density of imperfections at the SiC/SiO2 interface, which not only degrades the device performance but also causes reliability problems coming from the extreme operating conditions. The quality of the interface affects the channel mobility of MOSFETs, which is the most critical parameter of devices. In this work a hybrid functional density functional theory framework is employed to model the (0001)4H-SiC/SiO2 abrupt interface. Using this, defect energy levels in the bandgap have been calculated through the total and projected density of states. There is experimental evidence for improvement of the quality of the interface after passivation, However the atomic mechanisms of the improvement are not yet clear., Thus, the impact of various passivations on the potential defects has also been studied. Since the interface of SiC/SiO2 is not perfectly abrupt, several atomic configurations for (0001)4H-SiC/SiO2 transition layers have also been modeled, and their effect on the bandgap, and the near interface trap density has been studied. A DFT-based Monte Carlo carrier transport simulation technique is employed to compute the average velocities, phonon-limited and ionized-impurity-limited mobilities of the most probable transition layer structures. Finally, since low frequency noise calculation is a powerful tool to diagnose quality and reliability of semiconductor devices, a DFT-based method is presented to calculate the current spectral noise density of the (0001)4H-SiC/SiO2 transition layers

The Activation-Relaxation Technique : ART nouveau and kinetic ART

The evolution of many systems is dominated by rare activated events that occur on timescale ranging from nanoseconds to the hour or more. For such systems, simulations must leave aside the full thermal description to focus specifically on mechanisms that generate a configurational change. We present here the activation relaxation technique (ART), an open-ended saddle point search algorithm, and a series of recent improvements to ART nouveau and kinetic ART, an ART-based on-the-fly off-lattice self-learning kinetic Monte Carlo method