Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications

Kim, Chang-Eun; Moon, Pyung; Yun, Ilgu; Kim, Sung-Yeon; Myoung, Jae‐Min; Jang, Hyeon Woo; Bang, Jungsik

doi:10.1016/j.eswa.2010.08.074

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…Similar results, i.e. F TC = 0.13 -1.50 × 10 −2 Ω −1 for typical Al-doped ZnO, GZO and ITO thin films, have been reported in the literature [63][64][65]. Obviously, the value of F TC for the films grown at 400°C is observed to be very close to the highest F TC of the typical TCO candidate, indicating that the deposited thin films in our work have acceptable optical and electronic properties for potential applications as transparent conductive electrodes in photovoltaic solar cells, light emitting devices and flat panel displays.…”

Section: Electrical Propertiessupporting

confidence: 70%

Preparation, structure and optical properties of transparent conducting gallium-doped zinc oxide thin films

Long

et al. 2015

Materials Science-Poland

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Highly conductive gallium-doped zinc oxide (GZO) transparent thin films were deposited on glass substrates by RF magnetron sputtering. The deposited films were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), four-point probe and UV-Vis spectrophotometer, respectively. The effect of growth temperature on the structure and optoelectrical properties of the films was investigated. The results demonstrate that high quality GZO films oriented with their crystallographic c-axis perpendicular to the substrates are obtained. The structure and optoelectrical properties of the films are highly dependent on the growth temperature. It is found that with increasing growth temperature, the average visible transmittance of the deposited films is enhanced and the residual stress in the thin films is obviously relaxed. The GZO films deposited at the growth temperature of 400°C, which have the largest grain size (74.3 nm), the lowest electrical resistivity (1.31 × 10 −3 Ω·cm) and the maximum figure of merit (1.46 × 10 −2 Ω −1 ), exhibit the best optoelectrical properties. Furthermore, the optical properties of the deposited films were determined by the optical characterization methods and the optical energy-gaps were evaluated by extrapolation method. A blue shift of the optical energy gap is observed with an increase in the growth temperature.

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Section: Electrical Propertiessupporting

confidence: 70%

Preparation, structure and optical properties of transparent conducting gallium-doped zinc oxide thin films

Long

et al. 2015

Materials Science-Poland

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“…ANN and GA were also used in the modeling and optimization of ITO/Al/ITO multilayer films characteristics, with thin film thickness and the annealing temperature as the studied parameters [9]. ANN and GA had been applied in various other processes such as in the optimization of transmittance characteristic for indium tin oxide [10], optimization of process parameters for semiconductor compounds [11], optimization of TiO thin film process parameters [7], in the process estimation and optimized recipes of ZnO:Ga thin film characteristics [12] and in process parameters optimization for a titanium dioxide (TiO2) thin film [13]. Table 1 shows the brief summary on the optimization of process parameters in sputtering process based on soft computing techniques.…”

Section: Optimization and Prediction Based On Soft Computing Techniquesmentioning

confidence: 99%

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

Sabri

Puteh

Mahmood

2013

AMR

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This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

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“…In the recent years, artificial neural networks (ANNs), which are a data-driven technique from the field of machine learning, have been successfully applied in a wide variety of scientific fields to forecast various observables with accuracy and computational efficiency. − In the fields of chemical engineering and materials science, ANNs have been applied for the design of fuels and catalysts, predictions of gas composition, and other contexts. − Furthermore, studies that apply ANNs for stochastic multiscale systems have been recently published. ,, However, there appear to be a limited number of studies that use data-driven models to account for parametric uncertainty, conduct parameter estimation, and improve the quality of products of multiscale systems via feedback strategies and the identification of optimal control actions. Some of the currently existing examples include the usage of ANNs for the design and manufacturing of various thin films by vacuum or magnetron sputtering as well as plasma-enhanced chemical vapor deposition (CVD). − …”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Discrimination for Parameter Estimation and Optimal Product Design of Thin Films Manufactured by Chemical Vapor Deposition

Kimaev

Ricardez‐Sandoval

2020

J. Phys. Chem. C

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Industrial production of valuable chemical products often involves the manipulation of phenomena evolving at different temporal and spatial scales. Product properties can be captured accurately using computationally expensive stochastic multiscale models that explicitly consider the feedbacks between different scales. However, product design quality is often tampered by uncertainties affecting process operation. In this work, we used artificial neural networks (ANNs) to estimate an uncertain parameter, accurately predict product properties under uncertainty, and achieve orders-of-magnitude computational savings of a multiscale model of thin film formation by chemical vapor deposition. ANNs were trained using multiple realizations of the uncertain parameter to capture the behavior of the thin film’s two key microscale properties: roughness and growth rate. Next, mean square error and maximum likelihood estimation were used for parameter estimation and to find the ANN that could generate the closest predictions to the real-time measurements collected from the process in the presence of uncertainty. The chosen ANNs were employed to seek for the optimal operating conditions to enable the fabrication process to meet product quality specifications. ANNs are a promising technique for product property prediction and efficient decision making in the design of optimal operating conditions for chemical processes under uncertainty.

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Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications

Cited by 20 publications

References 16 publications

Preparation, structure and optical properties of transparent conducting gallium-doped zinc oxide thin films

Preparation, structure and optical properties of transparent conducting gallium-doped zinc oxide thin films

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

Artificial Neural Network Discrimination for Parameter Estimation and Optimal Product Design of Thin Films Manufactured by Chemical Vapor Deposition

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