Effects Of Deposition Process Parameters Research Articles

Study on growth rate of TiO2 nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network

Effects of the deposition process parameters on the thickness of TiO2 nanostructured film were simulated using the molecular dynamics (MD) approach and modeled by the artificial neural network (ANN) and regression method. Accordingly, TiO2 nanostructured film was prepared experimentally with the sol–gel dip‐coating method. Structural instabilities can be expected, due to short‐ and/or long‐range intermolecular forces, leading to the surface inhomogeneities. In the MD simulation, the Morse potential function was used for the inter‐atomic interactions, and equations of motion for atoms were solved by Verlet algorithm. The effect of the withdrawal velocity, drying temperature and number of deposited layers were studied in order to characterize the film thickness. The results of MD simulations are reasonably consistent with atomic force microscopy, scanning electron microscopy and Dektak surface profiler. Finally, the outputs from experimental data were analyzed by using the ANN in order to investigate the effects of deposition process parameters on the film thickness. In this case, various architectures have been checked using 75% of experimental data for training of the ANN. Among the various architectures, feed‐forward back‐propagation network with trainer training algorithm was found as the best architecture. Based on the R‐squared value, the ANN is better than the regression model in predicting the film thickness. The statistical analysis for those results was then used to verify the fitness of the complex process model. Based on the results, this modeling methodology can explain the characteristics of the TiO2 nanostructured thin film and growth mechanism varying with process conditions. © 2013 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.

Modeling of In 2O 3-10 wt% ZnO thin film properties for transparent conductive oxide using neural networks

Effects of deposition process parameters on the deposition rate and the electrical properties of In 2O 3–10 wt% ZnO (IZO) thin films were modeled and analyzed by using the error back-propagation neural networks (BPNN). Output models were represented by response surface plots and the fitness of models was estimated by calculating the root mean square error (RMSE). The deposition rate of IZO thin films is affected by the RF power and the substrate temperature. The electrical properties of the IZO thin films are mainly controlled by O 2 ratio and the substrate temperature. The predicted output characteristics by BPNN can sufficiently explain the mechanism of IZO deposition process. Thus, neural network models can provide the reliable explanation of IZO film deposition.

Effect of deposition process parameters on resistivity of metal and alloy films deposited using anodic vacuum arc technique

Thin solid films of copper, aluminum and nichrome have been deposited on glass substrates with thickness ranging from 20 nm to 200 nm, using the anodic vacuum arc deposition technique. Electrical resistivity and average grain size of deposited thin films have been measured and their dependence on the deposition process parameters has been investigated. Thickness dependence of resistivity has also been compared with numerically generated results using Fuchs–Sondheimer theory and Mayadas–Shatzkes theory which has been found to be in good agreement for film thickness greater than 80 nm. The resistivity values of Cu, Al and NiCr has been found to take a minimum value (approaching that of corresponding bulk material) of 80 A arc current and a substrate bias of around −50 V.

Vapor-Deposited Polyimide Ablators for NIF: Effects of Deposition Process Parameters and Solvent Vapor Smoothing on Capsule Surface Finish

Recent progress made at LLNL on fabricating NIF scale polyimide capsules using vapor deposition techniques is detailed. Our major focus has been on improving the capsule surf ace finish through better understanding of the origin of surface roughness created during the deposition process and implementation of a post-deposition vapor smoothing procedure prior to imidization. We have determined that the most important factors during the deposition process that impact surface finish include mandrel quality, monomer mixing, selfshadowing, and abrasion. We have shown that high rate deposition (above 10 μm/h) is effective at reducing roughness, which we believe is due to the shorter total time of shell agitation in the bouncer pan. By adjusting the coating conditions, coatings up to 160 μm thick have been reproduc-My fabricated with 300 nm RMS roughness. Solvent vapor smoothing, a new technique also developed at LLNL, further improves the surface to 30 nm RMS.

Effects of deposition process parameters on exchange coupling of α-Fe/sub 2/O/sub 3//NiFe bi-layers and GMR of α-Fe/sub 2/O/sub 3//NiFe/Cu/NiFe spin valves

Effects of deposition process parameters on the exchange coupling of /spl alpha/-Fe/sub 2/O/sub 3//NiFe bi-layers and GMR characteristics of /spl alpha/-Fe/sub 2/O/sub 3//NiFe/Cu/NiFe spin-valves using optimized anti-ferromagnetic /spl alpha/-Fe/sub 2/O/sub 3/ layer have been investigated. The exchange bias field was increased in /spl alpha/-Fe/sub 2/O/sub 3/(50 nm)/NiFe (7 nm) bi-layers by increasing oxygen partial pressure to 14.7%, substrate bias to 125 V, and input sputtering power up to 800 W of RF reactively sputtered /spl alpha/-Fe/sub 2/O/sub 3/ films. The thickness of NiFe and /spl alpha/-Fe/sub 2/O/sub 3/ layers also had an influence on the exchange coupling characteristics. By decreasing NiFe thickness from 35 nm to 5 nm and increasing /spl alpha/-Fe/sub 2/O/sub 3/ thickness from 550 nm, the exchange bias field dramatically increased up to 62.5 Oe. However, in the /spl alpha/-Fe/sub 2/O/sub 3/ thickness range of 50-100 nm, the exchange bias field decreased from 62.5 to 21.5 Oe. The GMR performance of as-deposited Si//spl alpha/-Fe/sub 2/O/sub 3/(50 nm)/NiFe(3.25 nm)/Cu(2 nm)/NiFe(3.25 nm)/Au (5.4 nm) spin valve structures was characterized. The CMR ratio and MR sensitivity was 5.2% and 0.6%/0e, respectively.

Correlating CVD Process Parameters and Film Properties

AbstractThis paper addresses an approach of predicting the effects of deposition process parameters on the film properties. The film resistivity is shown to correlate with other properties and testing conditions in explicit group form, and so is the rate of deposition with process parameters. Through a physical property relationship the two correlations merge to permit the prediction. Justifications for the correlations derive from both experimental and theoretical work already reported. Discussions on relative sensitivities of the variables toward another will follow.