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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 13, NO. 4, OCTOBER 2016
An Experimental Design for Processing Parameter Optimization for Cathode Arc Plasma Deposition of ZnO Films Shuo-Fu Hsu, Min-Hang Weng, Ru-Yuan Yang, Chun-Hsiung Fang, and Jyh-Horng Chou, Fellow, IEEE Abstract— An experimental design utilizing artificial neural networks (ANNs), the Taguchi method, and the genetic algorithm (GA) is proposed to obtain optimal processing parameters for cathode arc plasma deposition of ZnO thin films on a glass substrate. The Taguchi method’s orthogonal array is used to minimize the number of required experiments and to gather the experimental data. An ANN is then used to construct a system model based on the experimental data. Finally, the GA is used to determine the optimal process parameters. The average resistivity obtained from the optimal processing parameters is 3.19 × 10−3 -cm and the average transmittance obtained is 86.04%, both of which improve on results obtained using the Taguchi method alone (3.69 × 10−3 -cm and 85.41%). This indicates that the proposed design is a viable approach for determining the optimal process parameters. Note to Practitioners—This paper seeks to identify the optimal process parameters for the deposition of ZnO thin films on a glass substrate using cathode arc plasma deposition (CAPD). Current approaches for determining better process parameters for thin film deposition include trial-and-error or experimental design methods. However, these approaches are time-consuming, costly, and do not guarantee improved results. This paper combines the modeling and optimization methods to address this issue by using the artificial neural network, Taguchi method, and genetic algorithm to identify the optimal process parameters for a ZnO thin film using CAPD. Experimental results show considerable promise and the authors welcome feedback and questions. Index Terms— Artificial neural network (ANN), cathode arc plasma deposition (CAPD), genetic algorithm (GA), Taguchi method, ZnO thin film.
I. I NTRODUCTION
Z
nO oxide (ZnO) has attracted considerable research interest due to its wide and direct band gap (3.3 eV)
Manuscript received August 7, 2015; revised March 17, 2016; accepted May 19, 2016. Date of publication June 10, 2016; date of current version October 4, 2016. This paper was recommended for publication by Associate Editor D. Djurdjanovic and Editor H. Ding upon evaluation of the reviewers’ comments. This work was supported by the Ministry of Science and Technology, Taiwan, under Grant NSC 102-2221-E-151-021-MY3 and Grant MOST 105-2218-E-151-001. (Corresponding author: Jyh-Horng Chou.) S.-F. Hsu and C.-H. Fang are with the Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan (e-mail:
[email protected];
[email protected]). M.-H. Weng is with the Medical Devices and Opto-Electronics Equipment Department, Metal Industries Research and Development Center, Kaohsiung 811, Taiwan (e-mail:
[email protected]). R.-Y. Yang is with the Department of Materials Engineering, National Pingtung University of Science and Technology, Pingtung City 912, Taiwan (e-mail:
[email protected]). J.-H. Chou is with the Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, and also with the Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan (e-mail:
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TASE.2016.2572223
and excellent optoelectronic properties. They are desirable for application in optoelectronic devices, such as photodetectors, solar cells, light emitting diodes, gas sensors, varistors, ultraviolet laser diodes [1]. ZnO thin films also provide a processing advantage in terms of increased thermal stability and, thus, do not suffer from dislocation degradation during operation. Moreover, pure or doped ZnO thin films have been considered as good candidates for use as transparent conductive oxide (TCO) materials because of their good optical transmittance, low electrical resistivity, and low-cost fabrication [2]–[9]. Several different methods have been proposed to deposit ZnO films on glass for use as TCOs. These include chemical vapor deposition [2], thermal oxidation [3], radio frequency sputtering, magnetron sputtering [4], pulsed laser deposition [5], electron beam evaporation [6], spray pyrolysis [7], electrodeposition [8], and cathode arc plasma deposition (CAPD) [9]–[12]. Among these various deposition methods, CAPD has many advantages, including a high deposition rate, convenient in situ doping using an overlying plasma, and readily adjustable deposition parameters, such as substrate temperature, arc current, gas flow rate, and deposition time [9]–[12]. However, the CAPD processing of ZnO deposition entails many factors, including arc current (A), gas flow rates (Ar:O2 ), oxygen pressure, and deposition time (min). Thus, obtaining desirable electrical or optical properties in a ZnO film requires time-consuming and expensive experimentation which is not guaranteed to produce optimal results. The Taguchi method, widely used in industry to improve product quality [13]–[15], features an orthogonal array which is applied to significantly reduce required experimental operations by predicting the optimal combination of control factors. In the Taguchi method, a measure of robustness used to identify the control factors that reduce variability in a product or process by minimizing the effects of uncontrollable factors (noise factors). Control factors are those design and process parameters that can be controlled. Noise factors cannot be controlled during production or product use, but can be controlled during experimentation. In a Taguchi designed experiment, manipulate noise factors to force variability to occur and from the results, identify optimal control factor settings that make the process or product robust, or resistant to variation from the noise factors. Higher values of the signal-to-noise ratio (S/N) identify the control factor settings that minimize the effects of the noise factors. The S/N ratio measures how the response varies relative to the nominal or target value under different noise conditions. You can choose from different S/N ratios, depending on the goal of your experiment, including larger is better, Nominal is best, and Smaller is better. Artificial neural networks (ANNs) mimic the organization of neurons in the decision-making process of the human brain and are useful in pattern recognition, classification, and so on [16]. The ANN is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The ANNs derive their power from their massively parallel structure and an ability to learn from experience. An ANN can be trained to fairly classify input data to various categories. The knowledge
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HSU et al.: EXPERIMENTAL DESIGN FOR PROCESSING PARAMETER OPTIMIZATION FOR CAPD OF ZnO FILMS
gained by the learning experience is stored in the form of connection weights, which are used to make decisions for new outputs [17]–[19]. A multilayer ANN can approximate any nonlinear continuous function to an arbitrary accuracy [20]–[22]. It is good at elastically absorbing such uncertainties instead of learning and providing self-improvement internal feedback structures. So, the ANN has been applied in various real-world cases [23]–[28]. However, for training the ANN model, the experimental data collected by the Taguchi method are adequate, balanced, and orthogonal [14]. The ANN is utilized for a systematic method to create a datadriven model from a given set of input–output data. The genetic algorithm (GA) is a powerful method for finding the optimal solutions. Then, the GA is used to optimize the design parameters based on the data-driven system model obtained by using the ANN. The GA achieves optimization through mimicking processes observed in natural evolution [29]. The GA is stochastic search technique based on the mechanism of natural selection and natural genetics. To solve a problem, the GA maintains a population of individuals and probabilistically modifies the population through specific genetic operators, such as selection, crossover, and mutation to find a nearoptimal solution [29]. Other studies have modeled sputtering systems using the Taguchi method and ANNs [30] as well as used GA to search for optimal processing parameters [31]. Our previous work used a prototypical Taguchi method combined with gray relational analysis to evaluate process parameters for multiple characteristics in the deposition of ZnO thin films on glass substrates using CAPD [32]. However, this gray-relational Taguchi method can only obtain the optimal combination of processing parameters based on the defined level, such as a gas flow rate of 1:8, an arc current of 50 A, and a deposition time of 10 min [32]. Thus, in this paper, we combine the modeling and optimization methods to propose a novel processing parameter optimization design. First, the required quality performance and control factor levels are defined. Then, the appropriate orthogonal array in the Taguchi method is used to complete experiments and collect data representing performance quality. Next, we frame the system model utilizing the ANN model based on the data obtained from the Taguchi experimental method. Finally, the GA is used to identify the optimal process parameters. Besides, this methodology does not optimize ZnO film properties jointly. Each property is optimized individually.
II. M ODELING FOR C ATHODE A RC P LASMA D EPOSITION OF ZnO T HIN F ILMS Typically, thin-film deposition requires three sequential steps: providing a source of film material, transporting the material to the substrate, and deposition [11]. In this paper, ZnO thin films were prepared by CAPD to minimize resistivity and maximize transmittance. Deposition behavior is determined by the material source (Zn and O2 ) and transport factors as well as by the conditions at the deposition surface. However, film quality also depends on various process parameters in the CAPD system. For instance, the arc current influences the ionization rate of the zinc target as well as the deposition rate [9]. In undoped ZnO films, the electrons are typically attributed to intrinsic donors. The zinc interstitials and oxygen vacancies were commonly thought as the source of electrons. Recently, it was proved that especially in undoped ZnO, the zinc interstitialis the major source of electrons [10]. The gas flow rate (Ar:O2) influences the oxygen concentration in the deposited ZnO thin films. The oxygen pressure dominates the mean kinetic energy of zinc ions due to collisions with oxygen gas. It is known that the increase of oxygen pressure in the chamber
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TABLE I PARAMETER S ETTINGS AND C ONTROL FACTOR L EVELS
improves the stoichiometry of the films and the crystal quality [11]. However, the increase of the gas flow rate (Ar:O2) would decrease the kinetic energy of the reactive particles, reducing surface diffusion of the growing atoms as well as the film quality [10], [11]. The deposition time affects the thickness of the resulting thin film. Thus, the arc current and oxygen pressure affect the growth rate of the ZnO thin film [32]. We, therefore, defined four control factors and their levels in the ZnO films deposition process by CAPD. Factors included the arc current (60, 70, and 80 A), gas flow rates (Ar:O2 of 1:6, 1:8, and 1:10), oxygen pressure (1, 3, and 5 kg/cm3 ), and deposition time (5, 10, and 15 min). Parameter settings and control factor levels are summarized in Table I. For CAPD we used, sample revolution-per-minute (RPM) and substrate-to-cathode distance are constant. Furthermore, we only introduced O2 and Ar as reactant gas during deposition, that is to say, O2 and Ar gas flow meant total flow in this paper. Therefore, we chose above four adjustable processing parameters to complete the experimental design in this paper, including arc current, gas flow ratio, oxygen pressure, and deposition time. In the experiment, significant costs comprise the O2 and Ar gas, zinc target, electricity bills, and time. However, based on above parameter combinations, in order to obtain optimal parameters of transmittance or resistivity, it needs to complete a very large number of experiments. In other words, it is fabulous in total costs. Having this in mind, we proposed the experimental design to minimize the number of required experiments in this paper, hoping to achieve the purpose of cost saving.
III. E XPERIMENTAL D ETAILS ZnO films were deposited onto a glass substrate in a CAPD system. As the cathode target, metallic Zn with a diameter of 100 mm and a purity of 99.99% was held in an alumina ceramic tube. High purity O2 (99.99%) was used as the reactant gas. Before deposition, glass substrates were washed with alcohol and then ultrasonically cleaned in alcohol for 10 min. During deposition, the working pressure was kept at 3 × 10−4 torr, the substrate rotation speed was held at 2 RPM, and the substrate-anode electrode distance was maintained at ∼21 cm. The plasma is mainly produced from argon. To maintain a stable plasma environment and minimize variation in resulting film quality, the argon flow rate was fixed at 20 standard-cubic-centimeter-per-minute. Too low a gas flow rate in the chamber would prevent plasma generation. Deposition was performed at room temperature (
