Simulation and control of aggregate surface morphology in a two-stage thin film deposition process for improved light trapping

a b s t r a c t This work focuses on the development of a model predictive control algorithm to simultaneously regulate the aggregate surface slope and roughness of a thin film growth process to optimize thin film light reflectance and transmittance. Specifically, a two-stage thin film deposition process, which involves two microscopic processes: an adsorption process and a migration process, is modeled based on a one-dimensional solid-on-solid square lattice. The first stage of this process utilizes a uniform deposition rate profile to control the thickness of the thin film and the second stage of the process utilizes a spatially distributed deposition rate profile to control the surface morphology of the thin film. Kinetic Monte Carlo (kMC) methods are used to simulate this two-stage thin film deposition process. To characterize the surface morphology and to evaluate the light trapping efficiency of the thin film, aggregate surface roughness and slope corresponding to length scale of visible light are introduced as the root-mean squares of the aggregate surface height profile and aggregate surface slope profile. An Edwards-Wilkinson (EW)-type equation with appropriately computed parameters is used to describe the dynamics of the surface height profile and predict the evolution of the aggregate root-mean-square (RMS) roughness and aggregate RMS slope. A model predictive control algorithm is then developed on the basis of the EW equation model to regulate the aggregate RMS slope and the aggregate RMS roughness at desired levels. Closed-loop simulation results demonstrate the effectiveness of the proposed model predictive control algorithm in successfully regulating the aggregate RMS slope and the aggregate RMS roughness at desired levels that optimize thin film light reflectance and transmittance

One-Step Synthesis of Sulfur-Doped Nanoporous Carbons from Lignin with Ultra-High Surface Area, Sulfur Content and CO2 Adsorption Capacity

Lignin is the second-most available biopolymer in nature. In this work, lignin was employed as the carbon precursor for the one-step synthesis of sulfur-doped nanoporous carbons. Sulfur-doped nanoporous carbons have several applications in scientific and technological sectors. In order to synthesize sulfur-doped nanoporous carbons from lignin, sodium thiosulfate was employed as a sulfurizing agent and potassium hydroxide as the activating agent to create porosity. The resultant carbons were characterized by pore textural properties, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The nanoporous carbons possess BET surface areas of 741–3626 m2/g and a total pore volume of 0.5–1.74 cm3/g. The BET surface area of the carbon was one of the highest that was reported for any carbon-based materials. The sulfur contents of the carbons are 1–12.6 at.%, and the key functionalities include S=C, S-C=O, and SOx. The adsorption isotherms of three gases, CO2, CH4, and N2, were measured at 298 K, with pressure up to 1 bar. In all the carbons, the adsorbed amount was highest for CO2, followed by CH4 and N2. The equilibrium uptake capacity for CO2 was as high as ~11 mmol/g at 298 K and 760 torr, which is likely the highest among all the porous carbon-based materials reported so far. Ideally adsorbed solution theory (IAST) was employed to calculate the selectivity for CO2/N2, CO2/CH4, and CH4/N2, and some of the carbons reported a very high selectivity value. The overall results suggest that these carbons can potentially be used for gas separation purposes

Modeling and control of transparent conducting oxide layer surface morphology for improved light trapping.

a b s t r a c t This work first introduces a kinetic Monte-Carlo simulation model for a two species thin film deposition process and demonstrates the use of feedback control, coupled with a suitable actuator design, in manufacturing thin films whose surface morphology has a structure that improves light trapping. This work is relevant in the context of a Transparent Conducting Oxide (TCO) thin film layer manufacturing used in thin film solar cells where it is desirable to produce thin films with precisely tailored surface morphology. Specifically, a two species thin film deposition process involving atom adsorption, surface relaxation and surface migration is initially considered and is modeled using a large-lattice (lattice size ¼ 40,000) kinetic Monte-Carlo simulation. Subsequently, thin film surface morphology characteristics like roughness and slope are computed with respect to different characteristic length scales ranging from atomic to the ones corresponding to visible light wavelength and it is found that a patterned actuator design is needed to induce thin film surface roughness and slope at visible light wavelength spatial scales, that lead to desired thin film solar cell performance. Then, an Edwards-Wilkinson type equation is used to model the surface evolution at the visible light wavelength spatial scale and form the basis for the design of a predictive feedback controller whose objective is to manipulate the deposition rate across the spatial domain of the process. The model parameters of the Edwards-Wilkinson equation are estimated from kinetic Monte-Carlo simulations and their dependence on the deposition rate is used in the formulation of the predictive controller to predict the influence of the control action on the surface roughness and slope throughout the thin film growth process. Analytical solutions of the expected surface roughness and surface slope at the visible light wavelength spatial scale are obtained by solving the Edwards-Wilkinson equation and are used in the predictive controller formulation and in the control action calculation. The controller is applied to the large-lattice kinetic Monte-Carlo simulation. Simulation results demonstrate that the proposed controller and patterned actuator design successfully regulate aggregate surface roughness and slope to set-point values at the end of the deposition

Multiscale computational fluid dynamics modeling of an area-selective atomic layer deposition process using a discrete feed method

Area-selective atomic layer deposition (AS-ALD) is a beneficial procedure that facilitates self-alignment for transistor stacking by concentrating oxide growth on targeted areas of a substrate. However, AS-ALD is difficult to incorporate into semiconductor manufacturing industries due to difficulties such as minimal process data and a lack of insight into reactor design. To enable the industrial scale-up of AS-ALD, in silico modeling is necessary to characterize the process. Thus, this work proposes a multiscale computational fluid dynamics modeling framework that simultaneously describes the surface chemistry and ambient fluid behavior for an Al2O3/SiO2 substrate. The multiscale model first involves ab initio molecular dynamics simulations to optimize molecular structures involved in the AS-ALD reactions. Next, a kinetic Monte Carlo simulation is performed to describe the stochastic surface chemistry behavior to determine the surface coverage, and deposition and byproduct rates. Lastly, computational fluid dynamics is performed to study the spatiotemporal behavior of the flow. The surface and flow field simulations are carried out in an integrated fashion. Various AS-ALD discrete feed reactor configurations with differing injection plate geometries were developed to investigate their impact on the processing time to achieve full surface coverage and film uniformity. Results indicate that the multi-inlet reactor model achieves minimal processing time while producing a high-quality film with the AS-ALD process

Analysis of Nanoparticle Agglomeration in Aqueous Suspensions via Constant-Number Monte Carlo Simulation

A constant-number direct simulation Monte Carlo (DSMC) model was developed for the analysis of nanoparticle (NP) agglomeration in aqueous suspensions. The modeling approach, based on the “particles in a box” simulation method, considered both particle agglomeration and gravitational settling. Particle–particle agglomeration probability was determined based on the classical Derjaguin–Landau–Verwey–Overbeek (DLVO) theory and considerations of the collision frequency as impacted by Brownian motion. Model predictions were in reasonable agreement with respect to the particle size distribution and average agglomerate size when compared with dynamic light scattering (DLS) measurements for aqueous TiO₂, CeO₂, and C₆₀ nanoparticle suspensions over a wide range of pH (3–10) and ionic strength (0.01–156 mM). Simulations also demonstrated, in quantitative agreement with DLS measurements, that nanoparticle agglomerate size increased both with ionic strength and as the solution pH approached the isoelectric point (IEP). The present work suggests that the DSMC modeling approach, along with future use of an extended DLVO theory, has the potential for becoming a practical environmental analysis tool for predicting the agglomeration behavior of aqueous nanoparticle suspensions

Nanoporous Boron Nitride as Exceptionally Thermally Stable Adsorbent: Role in Efficient Separation of Light Hydrocarbons

In this work, nanoporous boron nitride sample was synthesized with a Brunauer–Emmett–Teller (BET) surface area of 1360 m²/g and particle size 5–7 μm. The boron nitride was characterized with X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and electron microscopy (TEM and SEM). Thermogravimetric analysis (TGA) under nitrogen and air and subsequent analysis with XPS and XRD suggested that its structure is stable in air up to 800 °C and in nitrogen up to 1050 °C, which is higher than most of the common adsorbents reported so far. Nitrogen and hydrocarbon adsorption at 298 K and pressure up to 1 bar suggested that all hydrocarbon adsorption amounts were higher than that of nitrogen and the adsorbed amount of hydrocarbon increases with an increase in its molecular weight. The kinetics of adsorption data suggested that adsorption becomes slower with the increase in molecular weight of hydrocarbons. The equilibrium data suggested that that boron nitride is selective to paraffins in a paraffin–olefin mixture and hence may act as an “olefin generator”. The ideal adsorbed solution theory (IAST)-based selectivity for CH₄/N₂, C₂H₆/CH₄, and C₃H₈/C₃H₆ was very high and probably higher than the majority of adsorbents reported in the literature. IAST-based calculations were also employed to simulate the binary mixture adsorption data for the gas pairs of CH₄/N₂, C₂H₆/CH₄, C₂H₆/C₂H₄, and C₃H₈/C₃H₆. Finally, a simple mathematical model was employed to simulate the breakthrough behavior of the above-mentioned four gas pairs in a dynamic column experiment. The overall results suggest that nanoporous boron nitride can be used as a potential adsorbent for light hydrocarbon separation