Visualization of atom's orbits.

High-resolution imaging techniques have been used to obtain views of internal shapes of single atoms or columns of atoms. This review article focuses on the visualization of internal atomic structures such as the configurations of electron orbits confined to atoms. This is accomplished by applying visualization techniques to the reported images of atoms or molecules as well as static and dynamic ions in a plasma. It was found that the photon and electron energies provide macroscopic and microscopic views of the orbit structures of atoms, respectively. The laser-imaged atoms showed a rugged orbit structure, containing alternating dark and bright orbits believed to be the pathways for an externally supplied laser energy and internally excited electron energy, respectively. By contrast, the atoms taken by the electron microscopy provided a structure of fine electron orbits, systematically formed in increasing order of grayscale representing the energy state of an orbit. This structure was identical to those of the plasma ions. The visualized electronic structures played a critical role in clarifying vague postulates made in the Bohr model. Main features proposed in the atomic model are the dynamic orbits absorbing an externally supplied electromagnetic energy, electron emission from them while accompanying light radiation, and frequency of electron waves not light. The light-accompanying electrons and ionic speckles induced by laser light signify that light is composed of electrons and ions.

Visual montioring of large plasma.

A visual system for monitoring transient plasma is presented. Dark particle distributions caught by the system proved effective for monitoring a large plasma space. A distrribution of electron orbits revealed that dark and less dark partices match ions and electrons, respectively. Light radiation evolved spatially from the electrode in the upward direction. Axial distributions of particle counts separated an entire discharge space into a plasma body, presheath and sheath. The axial sheath structure showed a neagative layer on an electrode. The axial particle profile was close to the typical one of electron number density. Illustrated was a real-time transient behavior of axial particle profile.

Impact of duty ratio-controlled ion energy on surface roughness of silicon nitride films deposited using a SiH4-NH3 plasma.

The impact of duty ratio-controlled ion energy on the surface roughness of silicon nitride (SiN) films is examined as a function of the bias power and the duty ratio. Experimental ranges are 30-90 W and 20-80% for the bias power and the duty ratio, respectively. SiN films were deposited at room temperature using a SiH4-NH3 pulsed plasma. Atomic force microscopy was used to measure surface roughness. Investigation is detailed in view of a mean-surface roughness, a non-uniformity of surface height distribution, and using a prediction model. The prediction model was constructed using a neural network and a genetic algorithm. A decrease in the duty ratio results in an increase in the surface roughness, but a decrease in the non-uniformity. Correlation study revealed that the surface roughness was strongly related to the ion energy and the ion energy flux. The neural network model predicts the high ion energy as the most influential diagnostic parameter.

Radio frequency source power-induced ion energy impact on SiN films deposited using a room temperature SiH4-N2 plasma.

Using a SiH4-N2 plasma, silicon nitride films were deposited at room temperature. The impact of source power ranging from 500 to 900 W and ion energy are investigated. The film properties examined include a deposition rate, a refractive index, and a surface roughness. Ion energy diagnostics was conducted to explore the relationships between ion energy and film properties. A variation in ion energy with source power was quite complex. By contrast, a decrease in ion energy flux was observed for a decrease in the source power. An increase in the deposition rate with the decrease in source power was attributed to enhanced ion energy. The refractive index strongly correlated with low ion energy flux. A decrease in surface roughness in the range of 500-700 W was related to larger ion energy. The deposition rate, refractive index, and surface roughness were varied in the range of 0.27-0.35 nm/sec, 1.690-1.739, and 6.7-52.5 nm, respectively.

The removal of nitrate by nanoscale iron particles produced using the sodium borohydride method.

This study was conducted to investigate removal of nitrate by nanoscale zero-valent iron (ZVI) particles in aqueous solution. ZVI particles was produced from wasted acid that is by-products of a pickling line at a steel work. The reaction activity of ZVI particles was evaluated through decomposition experiments of NO3-N aqueous solution. Addition of a larger amount of ZVI particles resulted in a higher decomposition rate. ZVI particles showed higher decomposition efficiencies than commercially purchased ZVI particles at all pH values. Both ZVIs showed a higher decomposition rate at a lower pH. Virtually no decomposition reaction was observed at pH of 4 or higher for purchased ZVI. The ZVI particles produced directly from wasted acid by the sodium borohydride method were not easy to handle because they were very small (10-200 nm) and were oxidized easily in the air.

Prediction on the seasonal behavior of hydrogen sulfide using a neural network model.

Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H2S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H2S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H2S. It was noted that H2S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H2S can be accounted for by a diverse combination of meteorological parameters across seasons.

Optimization of principal-component-analysis-applied in situ spectroscopy data using neural networks and genetic algorithms.

A new model of multidimensional in situ diagnostic data is presented. This was accomplished by combining a back-propagation neural network (BPNN), principal component analysis (PCA), and a genetic algorithm (GA). The PCA was used to reduce input dimensionality. The GA was applied to search for a set of optimized training factors involved in BPNN training. The presented technique was evaluated with optical emission spectroscopy (OES) data measured during the etching of oxide thin films in a CHF(3)-CF(4) inductively coupled plasma. For a systematic modeling, the etching process was characterized by a face-centered Box Wilson experiment. The etch responses to be modeled include oxide etch rate, oxide profile angle, and oxide etch rate non-uniformity. In PCA, three types of data variances were employed and the reduced input dimensionality corresponding to 100, 99, and 98% are 16, 8, and 5. The BPNN training factors to be optimized include the training tolerance, number of hidden neurons, magnitude of initial weight distribution, gradient of bipolar sigmoid function, and gradient of linear function. The prediction errors of GA-BPNN models are 249 A/min, 2.64 degrees, and 0.439% for the etch rate, profile angle, and etch rate non-uniformity, respectively. Compared to the conventional and previous full OES models, the presented models demonstrated a significantly improved prediction for all etch responses.

Surface morphology of SiN film deposited by a pulsed-plasma enhanced chemical vapor deposition at room temperature.

Silicon nitride (SiN) films were deposited by a pulsed plasma enhanced chemical vapor deposition system in a SiH4-NH3 chemistry. Surface morphology of SiN films at room temperature is first reported. Scanning electron microscope and atomic force microscopy were used for characterization. Radio frequency source power was varied from 200-800 W with an increment of 200 W. For each power, duty cycle was controlled as 40, 50, 70, 90%. Particularly, surface roughness was detailed in terms of a distribution of maximum pixel size or major pixel density, and a nonuniformity of pixel density. A consistent decrease in surface roughness with reducing duty cycle was observed in the ranges of 40-70% and 40-90% at 200 and 600 W, respectively. In contrast, surface roughness increased with reducing duty cycle at 800 W. Meanwhile, both maximum pixel size and distribution of major pixel density were highly correlated to surface roughness as a function of duty cycle at all powers. These two metrics are expected to effectively characterize the degree of surface densification as well as to support surface roughness variations.

Understanding for controls of particle shape of various titanates with layered structure.

In this study, various sized and shaped titanates were prepared using rutile phase TiO2 nano-powders in strong basic solution of NaOH having various metallic ions as chlorides by hydrothermal process. Obtained powders were fully characterized using SEM, TEM, XRD, and BET. The XRD results show that all obtained powders have layered structure. However, the shapes of particles doped with Zr4+ and Li+ show nano-belt and nano-plate, respectively, compared to those with nano-tubes of undoped, Ni2+ and Fe3+ doped. These results suggest that particle shape of titanates can be controlled only by small amount of doping elements in NaOH aqueous solutions.

Prediction of surface roughness using X-ray photoelectron spectroscopy and neural networks.

A new model for controlling plasma processes was constructed by combining atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and neural networks. The applicability of XPS to modeling etch rate was also investigated, as well as the impact of dc bias inclusion. The back-propagation neural network was used to find complex relationships between XPS and AFM data. This technique was evaluated with the etching data characterized by a 2(4) full factorial experiment. Five prediction models of surface roughness were constructed and compared. The Type I model refers to the model constructed with conventional process parameters. The Type II and III models were built with XPS and XPS plus dc bias data, respectively. The remaining Type IV and V models refer to those constructed with principal component analysis (PCA) reduced-XPS and PCA reduced-XPS plus dc bias, respectively. Mode prediction performance was evaluated as a function of training factor. In predicting the surface roughness, the Type II model yielded an improved prediction of 39% with respect to the Type IV model. The improvement was also demonstrated in modeling the etch rate. These results indicate that utilizing full XPS data is more effective for improving the model prediction performance. The advantage of XPS data was more conspicuous in constructing the surface roughness model.

Monitoring of surface roughness of plasma-deposited films using optical microscopy-measured dark particle distributions.

We present a dark field microscopy for the measurement of film surface roughness. The underlying principle is that a darker region in a film surface represents a deeper place from the top surface. The performance of the imaging system was demonstrated for silicon nitride films deposited in a SiH4-NH3 plasma. The system provided distributions of particles and the tendency of particle counts was compared with that of AFM-measured surface roughness. A strong correlation identified between them represents that the system is a viable alternative to AFM. The system is expected to find applications to roughness measurement during a real-time plasma processes.

Fuzzy logic model of Langmuir probe discharge data.

Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2(4) full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl2 flow rate. The other called multipole ICP was characterized by performing a 3(3) full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density. electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.