Young Don Ko

Formation of p-type ZnO film on InP substrate by phosphor doping

Abstract ZnO thin film was initially deposited on InP substrate by radio frequency (rf) magnetron sputtering and the diffusion process was performed using the closed ampoule technique where Zn3P2 was used as the dopant source. To verify the junction formation of ZnO thin films, the electrical properties were measured, and the effects of Zn3P2 diffusion on ZnO thin films were investigated. It is observed that the electrical property of the film is changed from n-type to p-type by dopant diffusion effect. Based on the results, it is confirmed that ZnO thin films can be a potential candidate for ultraviolet (UV) optical devices.

Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms

The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions.

Characteristics of ZnO/Si prepared by Zn3P2 diffusion

Abstract p-ZnO thin film formation on Si substrate is investigated using rf magnetron sputtering followed by Zn 3 P 2 diffusion process. In order to form p-ZnO thin film, n-ZnO thin film is initially deposited on Si substrate using rf magnetron sputtering. Then, Zn 3 P 2 source diffusion by closed ampoule technique is performed on ZnO/Si test structure. The electrical and optical characteristics of the ZnO thin films are investigated and the effect of Zn 3 P 2 diffusion on the properties of ZnO thin films are examined. From the analysis results, it is verified that p-type ZnO thin film on p-Si substrate is formed by dopants diffusion.

Neural network based modeling of HfO2 thin film characteristics using Latin Hypercube Sampling

Abstract In this paper, the neural network based modeling for electrical characteristics of the HfO 2 thin films grown by metal organic molecular beam epitaxy was investigated. The accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO 2 dielectric films. The input process parameters were extracted by analyzing the process conditions and the characterization of the films. X-ray diffraction was used to analyze the characteristic variation for the different process conditions. In order to build the process model, the neural network model using the error back-propagation algorithm was carried out and those initial weights and biases are selected by Latin Hypercube Sampling method. This modeling methodology can allow us to optimize the process recipes and improve the manufacturability.

Investigation of cell gap on the polymer substrates using statistical modelling for flexible liquid crystal display applications

The response surface model of the cell gap on the polymer substrates for the flexible Liquid Crystal Display (LCD) processes was investigated using statistical approaches. The D-optimal design is carried out to build the experimental runs considering the design space and the cell gap was characterised by the quadratic statistical models. The Analysis of Variance (ANOVA) technique was used to analyse the significance level among the factors. In addition, the desirability function for searching the specific conditions for the desired response was used for the design-space optimisation.

PCA-based network modeling using standardized X-ray diffraction data for the electrical characteristics of the HfO 2 thin films grown by MOMBE

In this paper, the PCA-based neural network process models of the HfO2 thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. Here, X-ray diffraction data that are standardized with mean and standard deviation. PCA is then carried out to reduce the dimension of the standardized two types of XRD data that are compressed into a small number of principal components. Those are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. The compressed data are trained using the neural networks.

PCA-Based neural network modeling using the photoluminescence data for growth rate of zno thin films fabricated by pulsed laser deposition

The process modeling for the growth rate of pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and PCA-based NNets using photoluminescence (PL) data. D-optimal experimental design was performed and the growth rate was characterized by NNets. PCA-based NNets were then carried out in order to build the model by PL data. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions and the model can be analyzed and predicted by the multivariate data.

Modeling growth rate of HfO2 thin films grown by metal–organic molecular beam epitaxy

Abstract HfO2 dielectric layers were grown on the p-type Si (100) substrate by metal–organic molecular beam epitaxy (MOMBE). Hafnium-tetra-butoxide, Hf(O·t-C4H9)4 was used as a Hf precursor and Argon gas was used as a carrier gas. The thickness of the HfO2 film and intermediate SiO2 layer were measured by scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HRTEM). The properties of the HfO2 layers were evaluated by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), high frequency (HF) capacitance–voltage (C–V) measurement, and current–voltage (I–V) measurement. C–V and I–V measurements have shown that HfO2 layer grown by MOMBE has a high dielectric constant (k) of 20–22 and a low-level of leakage current density. The growth rate is affected by various process variables such as substrate temperature, bubbler temperature, Ar and O2 gas flows and growth time. Since the ratio of O2 and Ar gas flows are closely correlated, the effect of variations in O2/Ar flow ratio on growth rate is also investigated using statistical modeling methodology.

Effect on the electrical properties of HfO/sub 2/ films grown by metal-organic molecular beam epitaxy

The characteristics of the HfO/sub 2/ dielectric layer on the p-type Si substrate by MOMBE process have been investigated. The electrical properties, surface morphology, and relative concentration of HfO/sub 2/ films could be tuned by O/sub 2//Ar ratio.

Neural network based modeling for the growth rate of ZnO thin films on the pulsed laser deposition

In this study the D-optimal design was used to make design matrix in this experiment. Neural networks (NNets) based on the backpropagation (BP) algorithm are applied to the pulsed laser deposition (PLD) process modeling in order to construct the model for the growth rate of the ZnO thin films.