Ignacio Martin-Bragado

Carbon in silicon: Modeling of diffusion and clustering mechanisms

Carbon often appears in Si in concentrations above its solubility. In this article, we propose a comprehensive model that, taking diffusion and clustering into account, is able to reproduce a variety of experimental results. Simulations have been performed by implementing this model in a Monte-Carlo atomistic simulator. The initial path for clustering included in the model is consistent with experimental observations regarding the formation and dissolution of substitutional C–interstitial C pairs (Cs–Ci). In addition, carbon diffusion profiles at 850 and 900 °C in carbon-doping superlattice structures are well reproduced. Finally, under conditions of thermal generation of intrinsic point defects, the weak temperature dependence of the Si interstitial undersaturation and the vacancy supersaturation in carbon-rich regions also agree with experimental measurements.

Facet formation during solid phase epitaxy regrowth: A lattice kinetic Monte Carlo model

An atomistic model to account for the formation of facets during solid phase epitaxy regrowth (SPER) is shown. This model relies on a lattice kinetic Monte Carlo approach. The lattice atoms produce different crystalline planes growing with different planar velocities. In particular, the model explains the arrow tip formation during SPER of thin silicon fins typical for fin field effect transistors and the formation of trenches in rectangular-shaped amorphized regions in (001) and (011) silicon, caused by the distortion of the lattice by shear strain and propagated by (111) facets.

Modeling arsenic deactivation through arsenic-vacancy clusters using an atomistic kinetic Monte Carlo approach

A comprehensive atomistic model for arsenic in silicon which includes charge effects and is consistent with first-principles calculations for arsenic-vacancy cluster energies has been developed. Emphasis has been put in reproducing the electrical deactivation and the annealed profiles in preamorphized silicon. The simulations performed with an atomistic kinetic Monte Carlo simulator suggest a predominant role of the mobile interstitial arsenic in deactivation experiments and provide a good understanding of the arsenic behavior in preamorphized silicon during annealing.

Atomistic Monte Carlo simulations of three-dimensional polycrystalline thin films

An atomistic Monte Carlo code to simulate the deposition and annealing of three-dimensional polycrystalline thin films is presented. Atoms impinge on the substrate with selected angular distributions, and grains are nucleated with different crystalline orientations, defined by the tilt and rotation angles. Grain boundaries appear naturally when the islands coalesce, and can migrate during both deposition and annealing simulations. In this work we present simulations of aluminum films. We examine the influence of the temperature, deposition rate, and adhesion to the substrate on the morphology of polycrystalline thin films. The simulations provide insight into the dominant microscopic mechanisms that drive the structure evolution during thin film processing.

Understanding Si(111) solid phase epitaxial regrowth using Monte Carlo modeling: Bi-modal growth, defect formation, and interface topology

This work studies the intriguing experimental observations that Si(111) solid phase epitaxial regrowth velocity is not constant as recrystallization progresses, but has a sudden change after recrystallization of ≈100 nm and progresses faster afterward.[L. Csepregi, J. W. Mayer, and T. W. Sigmon, Appl. Phys. Lett. 29(2), 92 (1976)] These two modes have important implications in the quality of the recrystallized silicon. The first recrystallization produces a flat advancing front leaving a heavy dense network of small and parallel to the surface twins behind, while the second mode creates a more rough advancing front that leaves bigger, although less dense inclined twins. By using a comprehensive and efficient lattice kinetic Monte Carlo model that explicitly accounts for the formation of different crystalline twin orientations, we simulate and expose the physical explanation of such observations. We explain the origin for the formation and subsequent evolution of different type of twins (parallel to the in...

Modeling of {311} facets using a lattice kinetic Monte Carlo three-dimensional model for selective epitaxial growth of silicon

Using lattice kinetic Monte Carlo, we provide a quantitative physically based atomistic model for the selective grown of Si-based materials, and explain {311} facet formation while remaining computationally efficient. Descriptions of the local atomistic configurations critical for the developing of {100}, {110}, {111}, and {311} facets are given. The model also introduces (a) three different microscopic growth rates for local {100} planes, needed to properly simulate the formation of perfect {100} terraces in miscut substrates and (b) a break-up energy to account for bonding during {311} facet formation. The model has been verified against experimental results.

Ion-beam amorphization of semiconductors: A physical model based on the amorphous pocket population

We introduce a model for damage accumulation up to amorphization, based on the ion-implant damage structures commonly known as amorphous pockets. The model is able to reproduce the silicon amorphous-crystalline transition temperature for C, Si, and Ge ion implants. Its use as an analysis tool reveals an unexpected bimodal distribution of the defect population around a characteristic size, which is larger for heavier ions. The defect population is split in both size and composition, with small, pure interstitial and vacancy clusters below the characteristic size, and amorphous pockets with a balanced mixture of interstitials and vacancies beyond that size.

An atomistic investigation of the impact of in-plane uniaxial stress during solid phase epitaxial regrowth

We propose an atomistic comprehensive model based on a lattice kinetic Monte Carlo approach to analyse the impact of in-plane uniaxial stress during solid phase epitaxial regrowth. We observed no influence of tensile stress on the regrowth kinetics. In contrast, compressive stress leads to (i) a reduction of the macroscopic regrowth velocity, (ii) an enhancement of the amorphous/crystalline interface roughness, and (iii) defective Si formation. Our observations are in good agreement with experimental data from the literature. Our atomistic approach also clarifies the interpretation of the interface morphological instability based on the kinetics of microscopic events.

Fermi-level effects in semiconductor processing: A modeling scheme for atomistic kinetic Monte Carlo simulators

Atomistic process simulation is expected to play an important role for the development of next generations of integrated circuits. This work describes an approach for modeling electric charge effects in a three-dimensional atomistic kinetic Monte Carlo process simulator. The proposed model has been applied to the diffusion of electrically active boron and arsenic atoms in silicon. Several key aspects of the underlying physical mechanisms are discussed: (i) the use of the local Debye length to smooth out the atomistic point-charge distribution, (ii) algorithms to correctly update the charge state in a physically accurate and computationally efficient way, and (iii) an efficient implementation of the drift of charged particles in an electric field. High-concentration effects such as band-gap narrowing and degenerate statistics are also taken into account. The efficiency, accuracy, and relevance of the model are discussed.

Atomistic Front-End Process Modelling: A Powerful Tool for Deep-Submicron Device Fabrication

The complexity attained by current microelectronics process technology can hardly be handled with simulators based on the continuum approach. Over the last few years, atomistic Kinetic Monte Carlo has proven to be a new way to tackle the problems that arise as device dimension shrink into the deep submicron regime. We present some encouraging results of exploring the capabilities of this new process modelling approach.