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Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications
Expert Systems with Applications 38 (2011) 2823–2827
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications Chang Eun Kim a, Pyung Moon a, Ilgu Yun a,⇑, Sungyeon Kim b, Jae-Min Myoung b, Hyeon Woo Jang c, Jungsik Bang c a b c
School of Electrical and Electronic Engineering, Yonsei University, 262, Seongsanno, Seodaemoon-gu, Seoul 120-749, Republic of Korea Department of Materials Science and Engineering, Yonsei University, 262, Seongsanno, Seodaemoon-gu, Seoul 120-749, Republic of Korea LG Chem, Ltd./Research Park, 104-1 Moonji-Dong, Yuseng-Gu, Daejeon 305-380, Republic of Korea
a r t i c l e
i n f o
Keywords: Ga-doped zinc oxide Transparent conductive oxide Figure of merit Neural networks Genetic algorithm Optimization
a b s t r a c t Ga-doped zinc oxide (ZnO:Ga) thin films were prepared on glass substrate by magnetron sputtering at room temperature (RT) and thermally annealed in hydrogen atmosphere for 1 h. The effects of film thickness and annealing temperature on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films were analyzed and modeled using the artificial neural networks (NNets). The NNet models presented the good prediction on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films and it was found that the electrical and optical properties of ZnO:Ga thin films were enhanced by thermal annealing. After NNet models were verified, genetic algorithm (GA) was used to search the optimized recipe for the desired figure of merit of ZnO:Ga thin films. The methodology allows us to estimate the optimal process condition with a small number of experiments. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction For the transparent electrode application in solar cells, transparent conductive oxide (TCO) thin films based on indium tin oxide (ITO) have been investigated (Zhang, Dong, Xu, Zhao, & Wu, 2008). Recently, the research on ZnO thin films has been attracted due to low cost, nontoxicity and high stability in hydrogen plasma (Bhosle, Tiwari, & Narayan, 2006). ZnO is typically grown as an n-type semiconductor with energy bandgap of 3.37 eV and its electrical and optical properties are changed by doping conditions with impurities such as Ga, In and Al (Fortunato, Assuncao, Goncalves, et al., 2004; Kim & Park, 2001; Tohsophon & Sirikulrat, 2006). The performance of doped ZnO thin films can be enhanced by the thermal annealing process (Kuo, Chen, Lai, et al., 2006; Yu et al., 2005). However, since semiconductor manufacturing process such as annealing is nonlinear and complicated, intelligence modeling technique, such as neural networks (NNets), is efficient to describe the effect of process variables on properties of thin films. NNets can perform the highly complex mapping between input factors and output responses, thereby leading to the detailed relationships on nonlinear data (May & Spanos, 2006). Moreover, NNets can generalize the overall trends in functional relationships from the limited data.
⇑ Corresponding author. Tel.: +82 2 2123 4619; fax: +82 2 313 2879. E-mail address: [email protected] (I. Yun). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.08.074
In this paper, we reported the modeling and characterization for the sheet resistance, transmittance and figure of merit of ZnO:Ga thin films on thickness and annealing temperature using NNets. The process parameters were then optimized to acquire the desired value for figure of merit by using genetic algorithm (GA). 2. Experiments 2.1. Experimental details ZnO:Ga thin films were deposited on glass substrate by radio frequency (RF) magnetron sputtering method. GZO ceramics (ZnO: 95 wt%, Ga2O3: 5 wt%) was used as a target source. The chamber was evacuated to a base pressure of 2 106 Torr and the film was deposited at working pressure of 3 mTorr where Ar was used as a working gas. The film growth process was performed at room temperature where the RF sputtering power was fixed at 100 W. The thickness of the thin film was controlled by deposition time with the deposition rate of 0.1 nm/s and the thickness was confirmed by scanning electron microscope. The film thickness was varied in the range of 135 and 185 nm. After the deposition, the thin films were thermally annealed in hydrogen atmosphere for 1 h and the annealing process was performed at two different temperature levels: 200 and 350 °C. The sheet resistance of ZnO:Ga thin films were then calculated from the film thickness and the resistivity measured by the hall measurement using Van der Pauw method. The transmittance of the ZnO:Ga thin film was also
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characterized by the spectrophotometer in the wavelength range of 270–800 nm. 2.2. Modeling scheme The structure of general NNets is composed of input layer, hidden layer, and output layer where each layer is linked by the neurons. Each link has a weight and a bias which are modified during the network training procedure. As a training algorithm, the error back propagation algorithm was used. When input vector was entered into the input layer of NNets, it was transferred through the hidden layer and the output was calculated by summing the weighted input from the hidden layer with filtering using the activation function. The calculated output was compared with the measured values, and the network error was defined as the sum of squared error of these two values. The weights of the NNets were adjusted in the network by using gradient descent approach in order to minimize the network error. The output models of sheet resistance, transmittance and figure of merit of ZnO:Ga thin films were trained and tested by using NNets. After the NNet models were established, the results were represented by the response surface plots. In order to measure the fitness of predicted NNet models, the root mean square error (RMSE) of training and testing datasets were calculated. Optimization of annealing process parameters in figure of merit of ZnO:Ga thin films was then performed using GA. GA is the optimization technique that uses three genetic operations, such as reproduction, crossover, and mutation (Ko et al., 2009; May & Spanos, 2006). The principle of GA operation is the simple rotation of four steps, which are the creation of a population of strings, the evaluation of each string, the selection of the string using the fitness value, and the creation of new population of strings by genetic manipulation. It is one of the global search algorithms which can find the optimum point in nonlinear and complex semiconductor manufacturing process. 3. Results and discussion 3.1. Neural network modeling results The general factorial experimental design was used in the process modeling of ZnO:Ga thin films. The thin-film thickness and annealing temperature were selected as input variables and the order of experiments was randomized to balance out the any irrelevant effects that affect the performance of thin films. Table 1 shows the experimental design matrix of annealing process factors used in each run. The six experiments were trained by NNets and the additional three experiments were performed to verify the predicted NNet models. Fig. 1 shows the modeling results for sheet resistance, transmittance and figure of merit of ZnO:Ga thin films, where the square symbol (‘j’) represents the training data and the circle symbol (‘s’) represents the testing data. From Fig. 1, it is ver-
Table 1 The experimental design matrix of annealing process. Run
Thickness (nm)
Annealing temperature (°C)
Remark
1 2 3 4 5 6
160 160 135 185 185 135
200 350 200 200 350 350
Training
7 8 9
170 173 173
200 200 350
Testing
Fig. 1. The neural network modeling results for (a) sheet resistance, (b) optical transmittance, and (c) figure of merit for ZnO:Ga thin films.
ified that the linear relationship between the measured and the predicted outputs can be obtained indicating that the NNet model can predict the output responses at randomly selected values of process variables. In order to measure the fitness of NNet model, the RMSE of training and testing experiments were calculated and summarized in Table 2. The RMSE is defined as the following equation (Mayers & Montgomery, 1995):
"
n X RMSE ¼ 1=n ðPi M i Þ2 i¼1
!#1=2 ;
ð1Þ
C.E. Kim et al. / Expert Systems with Applications 38 (2011) 2823–2827 Table 2 RMSE values of training and testing experiments.
Sheet resistance Transmittance Figure of merit
RMSE of training data
RMSE of testing data
0.122 0.016 0.100
0.326 0.008 0.576
where Pi and Mi are ith predicted output and ith measured output, respectively, and n is the number of data. The RMSE of testing experiments were comparable to that of the training experiments in all output responses. From these results, it can be concluded that the NNet model verification for sheet resistance, transmittance and figure of merit of ZnO:Ga thin films can be sufficiently supported. 3.2. Electrical property Fig. 2(a) shows the response surface plot of sheet resistance from NNet model with film thickness and annealing temperature. The sheet resistance of ZnO:Ga thin film is calculated by dividing the resistivity with film thickness. The variation of sheet resistance by the thermal annealing is dependent on the film thickness and annealing temperature. After thermal annealing, the sheet resistance is decreased as the annealing temperature is increased for all film thickness. The decrease of sheet resistance by the thermal annealing is due to the increase of carrier concentration. In ZnO:Ga thin film, the carrier concentration is originated from the oxygen vacancies, interstitial zinc metal, and donor electrons of Ga (Fang, Li, & Yao, 2002). When the ZnO:Ga thin film is annealed, carrier
2825
concentration is increased by the generation of oxygen vacancies and interstitial Zn metals and by the desorption of oxygen vacancies on the grain boundary due to supplied thermal energy (Chen, Guan, Fang, & Zhao, 2005; Fang et al., 2002). It affects the decrease of resistivity and reduces the sheet resistance. The variation of sheet resistance is large for the film thickness when the annealing temperature is low. However, it is also sensitive to film thickness at high annealing temperature. It is explained by the effect of mobility due to crystallinity as well as carrier concentration. When the films were annealed at 200 °C, the full width at half maximum (FWHM) value of thin films was decreased as the film thickness was increased (Kim, Moon, Kim, et al., 2009). It indicates that the grain size of thin films was increased. Therefore, the sheet resistance is decreased due to the improved mobility by the reduction of grain boundary scattering. Fig. 2(b) shows the variation of sheet resistance in ZnO:Ga thin film annealed at 350 °C. At higher annealing temperature, the variation of sheet resistance is explained by the variation of mobility. As the film thickness increased from 135 to 160 nm, carrier concentration was decreased and FWHM was increased. The mobility was increased due to the decrease of impurity scattering rather than the increase of grain boundary scattering and the sheet resistance is decreased from 66.7 to 43.86 X/sq. When the film thickness increased from 160 to 185 nm, carrier concentration was increased and FWHM was invariant. The mobility was decreased due to the increase of impurity scattering and the sheet resistance increased from 43.86 to 54.1 X/sq. It is indicated that the optimal point for the lowest sheet resistance exists in the experimental space and is dependent on film thickness and annealing temperature. 3.3. Optical property Fig. 3(a) shows the transmission in UV–Vis–IR regions of asgrown ZnO:Ga thin film as a function of the film thickness. The transmittance of the thin film decreases with the increase of the film thickness since light is more absorbed in the film as the film thickness is increased. Fig. 3(b) shows the optical transmittance curves of the 170-nm-thick ZnO:Ga thin film with before and after annealing at 200 and 350 °C. When the annealing temperature is increased, the absorption edge moves to shorter wavelength and the average optical transmittance increases. The shift of absorption edge with annealing temperature is due to the increase of carrier concentration of thin films and is explained by the Burstein–Moss effect, where the conduction band becomes filled at high carrier concentration and the lowest energy states in the conduction band are blocked (Oh, Jeong, Kim, Lee, & Myoung, 2005; Sheu, Shu, Lee, Tun, & Chi, 2007). Fig. 4 shows the response surface plot of optical transmittance from NNet model with varying the film thickness and the annealing temperature. The higher transmittance can be obtained from the thin film annealed at 350 °C and it is due to the reduction of scattering of light by the enhancement of crystallinity in the thin film (Guillén & Herrero, 2007). As the film thickness is increased, the effect of thermal annealing on transmittance variation is more dominant. Thus, it can be concluded that the improvement of crystallinity in ZnO:Ga thin film by thermal annealing is sensitive to the film thickness. 3.4. Process optimization for figure of merit
Fig. 2. The (a) response surface plot of sheet resistance and the (b) electrical properties in ZnO:Ga thin film annealed at 350 °C.
For the practical ZnO:Ga thin film application for the transparent electrode in solar cells, the high transmittance and low sheet resistance are required. In order to estimate of quality of ZnO:Ga thin film, the figure of merit defined by Haacke (1976) is calculated as a criteria for performance of transparent conductive oxides and it is expressed by following equation:
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Fig. 5. The response surface plot of figure of merit.
Table 3 The optimization results of GA. Optimized results
Fig. 3. The (a) optical transmittance of as-grown ZnO:Ga thin films as a function of film thickness, and (b) optical transmittance of ZnO:Ga thin films which has 170 nm thickness before and after annealing at 200 and 350 °C.
Measured result
Thickness (nm)
Annealing temperature (°C)
Figure of merit (0.001/ X)
Thickness (nm)
Annealing temperature (°C)
Figure of merit (0.001/ X)
161.3 160.4
349.8 349.7
9.309 9.295
160
350
9.506
the annealing temperature is shown in Fig. 5. The figure of merit increases by annealing process due to the decrease of sheet resistance as well as the enhancement of transparency. In order to search the optimum process condition for the figure of merit, the GA is used and applied to the NNet model. The optimized recipes obtained from GA are summarized in Table 3. The acquired results are close to the actual measured values. Thus, the GA is an effective method to predict the desired process condition for the characteristics of the ZnO:Ga thin films. 4. Conclusions
Fig. 4. The response surface plot optical transmittance.
UTC ¼ T 10 =Rs ;
ð2Þ
where T is the optical transmittance at 550 nm and Rs is the sheet resistance. When annealing temperature is increased from 200 to 350 °C for the 160-nm-thick ZnO:Ga thin film, the figure of merit increases from 0.973 to 9.506. The response surface plot of figure of merit from NNet model with varying the film thickness and
The sheet resistance, the transmittance, and the figure of merit of the ZnO:Ga thin films annealed in hydrogen atmosphere have been characterized by general factorial experimental design and then modeled using back propagation neural networks. Based on the modeling results, the output responses at randomly selected values of process variables in the design space can be predicted by the NNets indicating that the NNet model can explain the processing effects of the ZnO:Ga thin films. The sheet resistance decreased as the annealing temperature was increased due to the increase of carrier concentration by the generated oxygen vacancies and interstitial Zn metals. The increase of transmittance by annealing was explained by the reduction of scattering. The figure of merit was improved by the low sheet resistance and high transmittance acquired by the annealing process. Finally, the optimal process conditions for figure of merit are predicted using the GA. The desired process condition was determined as the 160-nm thick thin film annealed at 350 °C and the sheet resistance of 43 X/sq, optical transmittance of above 90% and figure of merit of about 9 were obtained as the optimal characteristics.
C.E. Kim et al. / Expert Systems with Applications 38 (2011) 2823–2827
Acknowledgment This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. R01-2007-000-20143-0). S. Kim and J.M. Myoung acknowledge support from the Converging Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0093706).
References Bhosle, V., Tiwari, A., & Narayan, J. (2006). Electrical properties of transparent and conducting Ga doped ZnO. Journal of Applied Physics, 100, 033713. Chen, X., Guan, W., Fang, G., & Zhao, X. Z. (2005). Influence of substrate temperature and post-treatment on the properties of ZnO:Al thin films prepared by pulsed laser deposition. Applied Surface Science, 252, 1561–1567. Fang, G. J., Li, D., & Yao, B.-L. (2002). Influence of post-deposition annealing on the properties of transparent conductive nanocrystalline ZAO thin films prepared by RF magnetron sputtering with highly conductive ceramic target. Thin Solid Films, 418, 156–162. Fortunato, E., Assuncao, V., Goncalves, A., et al. (2004). High quality conductive gallium-doped zinc oxide films deposited at room temperature. Thin Solid Films, 451–452, 443–447. Guillén, C., & Herrero, J. (2007). Structure, optical, and electrical properties of indium tin oxide thin films prepared by sputtering at room temperature and annealed in air or nitrogen. Journal of Applied Physics, 101, 073514. Haacke, G. (1976). New figure of merit for transparent conductors. Journal of Applied Physics, 47, 4086.
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Kim, C. E., Moon, P., Kim, S., et al. (2009). Modeling of thermal annealing of ZnO:Ga thin films for transparent conductive oxide using neural networks. Proceedings of the IASTED International Conference Artificial Intelligence and Applications, 152–157. Kim, K. J., & Park, Y. R. (2001). Large and abrupt optical band gap variation in Indoped ZnO. Applied Physics Letter, 78, 475–477. Ko, Y.-D., Moon, P., Kim, C. E., Ham, M.-H., Myoung, J.-M., & Yun, I. (2009). Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms. Expert Systems with Applications, 36, 4061–4066. Kuo, S.-Y., Chen, W.-C., Lai, F.-I., et al. (2006). Effects of doping concentration and annealing temperature on properties of highly-oriented Al-doped ZnO films. Journal of Crystal Growth, 287, 78–84. May, G. S., & Spanos, C. J. (2006). Fundamentals of semiconductor manufacturing and process control. New Jersey: Wiley & Sons. Mayers, R. H., & Montgomery, D. C. (1995). Response surface methodology. New York: Wiley & Sons. Oh, B.-Y., Jeong, M.-C., Kim, D.-S., Lee, W., & Myoung, J.-M. (2005). Post-annealing of Al-doped ZnO films in hydrogen atmosphere. Journal of Crystal Growth, 281, 475–480. Sheu, J. K., Shu, K. W., Lee, M. L., Tun, C. J., & Chi, G. C. (2007). Effect of thermal annealing on Ga-doped ZnO films prepared by magnetron sputtering. Journal of the Electrochemical Society, 154, H521–H524. Tohsophon, T., & Sirikulrat, N. (2006). Effects of step-deposition on structures and properties of transparent conducting aluminum-doped zinc oxide films prepared by DC magnetron sputtering. Solar Energy Materials and Solar Cells, 90, 3444–3448. Yu, X., Ma, J., Ji, Fe., Wang, Y., Zhang, X., & Ma, H. (2005). Influence of annealing on the properties of ZnO:Ga films prepared by radio frequency magnetron sputtering. Thin Solid Films, 483, 296–300. Zhang, B., Dong, X., Xu, X., Zhao, P., & Wu, J. (2008). Characteristics of zirconiumdoped indium tin oxide thin films deposited by magnetron sputtering. Solar Energy Materials and Solar Cells, 92, 1224–1229.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications Chang Eun Kim a, Pyung Moon a, Ilgu Yun a,⇑, Sungyeon Kim b, Jae-Min Myoung b, Hyeon Woo Jang c, Jungsik Bang c a b c
School of Electrical and Electronic Engineering, Yonsei University, 262, Seongsanno, Seodaemoon-gu, Seoul 120-749, Republic of Korea Department of Materials Science and Engineering, Yonsei University, 262, Seongsanno, Seodaemoon-gu, Seoul 120-749, Republic of Korea LG Chem, Ltd./Research Park, 104-1 Moonji-Dong, Yuseng-Gu, Daejeon 305-380, Republic of Korea
a r t i c l e
i n f o
Keywords: Ga-doped zinc oxide Transparent conductive oxide Figure of merit Neural networks Genetic algorithm Optimization
a b s t r a c t Ga-doped zinc oxide (ZnO:Ga) thin films were prepared on glass substrate by magnetron sputtering at room temperature (RT) and thermally annealed in hydrogen atmosphere for 1 h. The effects of film thickness and annealing temperature on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films were analyzed and modeled using the artificial neural networks (NNets). The NNet models presented the good prediction on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films and it was found that the electrical and optical properties of ZnO:Ga thin films were enhanced by thermal annealing. After NNet models were verified, genetic algorithm (GA) was used to search the optimized recipe for the desired figure of merit of ZnO:Ga thin films. The methodology allows us to estimate the optimal process condition with a small number of experiments. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction For the transparent electrode application in solar cells, transparent conductive oxide (TCO) thin films based on indium tin oxide (ITO) have been investigated (Zhang, Dong, Xu, Zhao, & Wu, 2008). Recently, the research on ZnO thin films has been attracted due to low cost, nontoxicity and high stability in hydrogen plasma (Bhosle, Tiwari, & Narayan, 2006). ZnO is typically grown as an n-type semiconductor with energy bandgap of 3.37 eV and its electrical and optical properties are changed by doping conditions with impurities such as Ga, In and Al (Fortunato, Assuncao, Goncalves, et al., 2004; Kim & Park, 2001; Tohsophon & Sirikulrat, 2006). The performance of doped ZnO thin films can be enhanced by the thermal annealing process (Kuo, Chen, Lai, et al., 2006; Yu et al., 2005). However, since semiconductor manufacturing process such as annealing is nonlinear and complicated, intelligence modeling technique, such as neural networks (NNets), is efficient to describe the effect of process variables on properties of thin films. NNets can perform the highly complex mapping between input factors and output responses, thereby leading to the detailed relationships on nonlinear data (May & Spanos, 2006). Moreover, NNets can generalize the overall trends in functional relationships from the limited data.
⇑ Corresponding author. Tel.: +82 2 2123 4619; fax: +82 2 313 2879. E-mail address: [email protected] (I. Yun). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.08.074
In this paper, we reported the modeling and characterization for the sheet resistance, transmittance and figure of merit of ZnO:Ga thin films on thickness and annealing temperature using NNets. The process parameters were then optimized to acquire the desired value for figure of merit by using genetic algorithm (GA). 2. Experiments 2.1. Experimental details ZnO:Ga thin films were deposited on glass substrate by radio frequency (RF) magnetron sputtering method. GZO ceramics (ZnO: 95 wt%, Ga2O3: 5 wt%) was used as a target source. The chamber was evacuated to a base pressure of 2 106 Torr and the film was deposited at working pressure of 3 mTorr where Ar was used as a working gas. The film growth process was performed at room temperature where the RF sputtering power was fixed at 100 W. The thickness of the thin film was controlled by deposition time with the deposition rate of 0.1 nm/s and the thickness was confirmed by scanning electron microscope. The film thickness was varied in the range of 135 and 185 nm. After the deposition, the thin films were thermally annealed in hydrogen atmosphere for 1 h and the annealing process was performed at two different temperature levels: 200 and 350 °C. The sheet resistance of ZnO:Ga thin films were then calculated from the film thickness and the resistivity measured by the hall measurement using Van der Pauw method. The transmittance of the ZnO:Ga thin film was also
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C.E. Kim et al. / Expert Systems with Applications 38 (2011) 2823–2827
characterized by the spectrophotometer in the wavelength range of 270–800 nm. 2.2. Modeling scheme The structure of general NNets is composed of input layer, hidden layer, and output layer where each layer is linked by the neurons. Each link has a weight and a bias which are modified during the network training procedure. As a training algorithm, the error back propagation algorithm was used. When input vector was entered into the input layer of NNets, it was transferred through the hidden layer and the output was calculated by summing the weighted input from the hidden layer with filtering using the activation function. The calculated output was compared with the measured values, and the network error was defined as the sum of squared error of these two values. The weights of the NNets were adjusted in the network by using gradient descent approach in order to minimize the network error. The output models of sheet resistance, transmittance and figure of merit of ZnO:Ga thin films were trained and tested by using NNets. After the NNet models were established, the results were represented by the response surface plots. In order to measure the fitness of predicted NNet models, the root mean square error (RMSE) of training and testing datasets were calculated. Optimization of annealing process parameters in figure of merit of ZnO:Ga thin films was then performed using GA. GA is the optimization technique that uses three genetic operations, such as reproduction, crossover, and mutation (Ko et al., 2009; May & Spanos, 2006). The principle of GA operation is the simple rotation of four steps, which are the creation of a population of strings, the evaluation of each string, the selection of the string using the fitness value, and the creation of new population of strings by genetic manipulation. It is one of the global search algorithms which can find the optimum point in nonlinear and complex semiconductor manufacturing process. 3. Results and discussion 3.1. Neural network modeling results The general factorial experimental design was used in the process modeling of ZnO:Ga thin films. The thin-film thickness and annealing temperature were selected as input variables and the order of experiments was randomized to balance out the any irrelevant effects that affect the performance of thin films. Table 1 shows the experimental design matrix of annealing process factors used in each run. The six experiments were trained by NNets and the additional three experiments were performed to verify the predicted NNet models. Fig. 1 shows the modeling results for sheet resistance, transmittance and figure of merit of ZnO:Ga thin films, where the square symbol (‘j’) represents the training data and the circle symbol (‘s’) represents the testing data. From Fig. 1, it is ver-
Table 1 The experimental design matrix of annealing process. Run
Thickness (nm)
Annealing temperature (°C)
Remark
1 2 3 4 5 6
160 160 135 185 185 135
200 350 200 200 350 350
Training
7 8 9
170 173 173
200 200 350
Testing
Fig. 1. The neural network modeling results for (a) sheet resistance, (b) optical transmittance, and (c) figure of merit for ZnO:Ga thin films.
ified that the linear relationship between the measured and the predicted outputs can be obtained indicating that the NNet model can predict the output responses at randomly selected values of process variables. In order to measure the fitness of NNet model, the RMSE of training and testing experiments were calculated and summarized in Table 2. The RMSE is defined as the following equation (Mayers & Montgomery, 1995):
"
n X RMSE ¼ 1=n ðPi M i Þ2 i¼1
!#1=2 ;
ð1Þ
C.E. Kim et al. / Expert Systems with Applications 38 (2011) 2823–2827 Table 2 RMSE values of training and testing experiments.
Sheet resistance Transmittance Figure of merit
RMSE of training data
RMSE of testing data
0.122 0.016 0.100
0.326 0.008 0.576
where Pi and Mi are ith predicted output and ith measured output, respectively, and n is the number of data. The RMSE of testing experiments were comparable to that of the training experiments in all output responses. From these results, it can be concluded that the NNet model verification for sheet resistance, transmittance and figure of merit of ZnO:Ga thin films can be sufficiently supported. 3.2. Electrical property Fig. 2(a) shows the response surface plot of sheet resistance from NNet model with film thickness and annealing temperature. The sheet resistance of ZnO:Ga thin film is calculated by dividing the resistivity with film thickness. The variation of sheet resistance by the thermal annealing is dependent on the film thickness and annealing temperature. After thermal annealing, the sheet resistance is decreased as the annealing temperature is increased for all film thickness. The decrease of sheet resistance by the thermal annealing is due to the increase of carrier concentration. In ZnO:Ga thin film, the carrier concentration is originated from the oxygen vacancies, interstitial zinc metal, and donor electrons of Ga (Fang, Li, & Yao, 2002). When the ZnO:Ga thin film is annealed, carrier
2825
concentration is increased by the generation of oxygen vacancies and interstitial Zn metals and by the desorption of oxygen vacancies on the grain boundary due to supplied thermal energy (Chen, Guan, Fang, & Zhao, 2005; Fang et al., 2002). It affects the decrease of resistivity and reduces the sheet resistance. The variation of sheet resistance is large for the film thickness when the annealing temperature is low. However, it is also sensitive to film thickness at high annealing temperature. It is explained by the effect of mobility due to crystallinity as well as carrier concentration. When the films were annealed at 200 °C, the full width at half maximum (FWHM) value of thin films was decreased as the film thickness was increased (Kim, Moon, Kim, et al., 2009). It indicates that the grain size of thin films was increased. Therefore, the sheet resistance is decreased due to the improved mobility by the reduction of grain boundary scattering. Fig. 2(b) shows the variation of sheet resistance in ZnO:Ga thin film annealed at 350 °C. At higher annealing temperature, the variation of sheet resistance is explained by the variation of mobility. As the film thickness increased from 135 to 160 nm, carrier concentration was decreased and FWHM was increased. The mobility was increased due to the decrease of impurity scattering rather than the increase of grain boundary scattering and the sheet resistance is decreased from 66.7 to 43.86 X/sq. When the film thickness increased from 160 to 185 nm, carrier concentration was increased and FWHM was invariant. The mobility was decreased due to the increase of impurity scattering and the sheet resistance increased from 43.86 to 54.1 X/sq. It is indicated that the optimal point for the lowest sheet resistance exists in the experimental space and is dependent on film thickness and annealing temperature. 3.3. Optical property Fig. 3(a) shows the transmission in UV–Vis–IR regions of asgrown ZnO:Ga thin film as a function of the film thickness. The transmittance of the thin film decreases with the increase of the film thickness since light is more absorbed in the film as the film thickness is increased. Fig. 3(b) shows the optical transmittance curves of the 170-nm-thick ZnO:Ga thin film with before and after annealing at 200 and 350 °C. When the annealing temperature is increased, the absorption edge moves to shorter wavelength and the average optical transmittance increases. The shift of absorption edge with annealing temperature is due to the increase of carrier concentration of thin films and is explained by the Burstein–Moss effect, where the conduction band becomes filled at high carrier concentration and the lowest energy states in the conduction band are blocked (Oh, Jeong, Kim, Lee, & Myoung, 2005; Sheu, Shu, Lee, Tun, & Chi, 2007). Fig. 4 shows the response surface plot of optical transmittance from NNet model with varying the film thickness and the annealing temperature. The higher transmittance can be obtained from the thin film annealed at 350 °C and it is due to the reduction of scattering of light by the enhancement of crystallinity in the thin film (Guillén & Herrero, 2007). As the film thickness is increased, the effect of thermal annealing on transmittance variation is more dominant. Thus, it can be concluded that the improvement of crystallinity in ZnO:Ga thin film by thermal annealing is sensitive to the film thickness. 3.4. Process optimization for figure of merit
Fig. 2. The (a) response surface plot of sheet resistance and the (b) electrical properties in ZnO:Ga thin film annealed at 350 °C.
For the practical ZnO:Ga thin film application for the transparent electrode in solar cells, the high transmittance and low sheet resistance are required. In order to estimate of quality of ZnO:Ga thin film, the figure of merit defined by Haacke (1976) is calculated as a criteria for performance of transparent conductive oxides and it is expressed by following equation:
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Fig. 5. The response surface plot of figure of merit.
Table 3 The optimization results of GA. Optimized results
Fig. 3. The (a) optical transmittance of as-grown ZnO:Ga thin films as a function of film thickness, and (b) optical transmittance of ZnO:Ga thin films which has 170 nm thickness before and after annealing at 200 and 350 °C.
Measured result
Thickness (nm)
Annealing temperature (°C)
Figure of merit (0.001/ X)
Thickness (nm)
Annealing temperature (°C)
Figure of merit (0.001/ X)
161.3 160.4
349.8 349.7
9.309 9.295
160
350
9.506
the annealing temperature is shown in Fig. 5. The figure of merit increases by annealing process due to the decrease of sheet resistance as well as the enhancement of transparency. In order to search the optimum process condition for the figure of merit, the GA is used and applied to the NNet model. The optimized recipes obtained from GA are summarized in Table 3. The acquired results are close to the actual measured values. Thus, the GA is an effective method to predict the desired process condition for the characteristics of the ZnO:Ga thin films. 4. Conclusions
Fig. 4. The response surface plot optical transmittance.
UTC ¼ T 10 =Rs ;
ð2Þ
where T is the optical transmittance at 550 nm and Rs is the sheet resistance. When annealing temperature is increased from 200 to 350 °C for the 160-nm-thick ZnO:Ga thin film, the figure of merit increases from 0.973 to 9.506. The response surface plot of figure of merit from NNet model with varying the film thickness and
The sheet resistance, the transmittance, and the figure of merit of the ZnO:Ga thin films annealed in hydrogen atmosphere have been characterized by general factorial experimental design and then modeled using back propagation neural networks. Based on the modeling results, the output responses at randomly selected values of process variables in the design space can be predicted by the NNets indicating that the NNet model can explain the processing effects of the ZnO:Ga thin films. The sheet resistance decreased as the annealing temperature was increased due to the increase of carrier concentration by the generated oxygen vacancies and interstitial Zn metals. The increase of transmittance by annealing was explained by the reduction of scattering. The figure of merit was improved by the low sheet resistance and high transmittance acquired by the annealing process. Finally, the optimal process conditions for figure of merit are predicted using the GA. The desired process condition was determined as the 160-nm thick thin film annealed at 350 °C and the sheet resistance of 43 X/sq, optical transmittance of above 90% and figure of merit of about 9 were obtained as the optimal characteristics.
C.E. Kim et al. / Expert Systems with Applications 38 (2011) 2823–2827
Acknowledgment This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. R01-2007-000-20143-0). S. Kim and J.M. Myoung acknowledge support from the Converging Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0093706).
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