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Temperature effect on charge density of silicon nitride films deposited in SiH4–NH3–N2 plasma
Surface & Coatings Technology 202 (2008) 5539–5542
Contents lists available at ScienceDirect
Surface & Coatings Technology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / s u r f c o a t
Temperature effect on charge density of silicon nitride films deposited in SiH4–NH3–N2 plasma Byungwhan Kim ⁎, Sang Hee Kwon Department of Electronic Engineering, Sejong University, 98, Goonja-Dong, Kwangjin-Gu, Seoul, 143-747, Republic of Korea
A R T I C L E
I N F O
Available online 8 June 2008 PACS: 52.75.R 81.15.G Keywords: Silicon nitride film Plasma enhanced chemical vapor deposition Generalized regression neural network Charge density SiH4 Temperature
A B S T R A C T Improved surface passivation of silicon nitride films requires a high positive charge density. A neural network model of SiN charge density was used to investigate temperature effects on charge density. For a systematic modeling, the deposition process was characterized by means of a face-centered Box Wilson experiment. Prediction performance of neural network model was optimized by using genetic algorithm. Interestingly, charge density was varied little with the substrate temperature regardless of SiH4 flow rates. Charge density variation was not sensitive to [Si–H] variation. A pronounced temperature effect at higher NH3 flow rate or lower radio frequency (rf) power was attributed to a relatively large [N–H]. For NH3 or rf power variation, charge density was strongly correlated to [N–H]/[Si–H]. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Silicon nitride (SiN) films are widely used as a passivation layer in fabricating solar cells. Using a plasma-enhanced chemical vapor deposition (PECVD) system, various SiN films were deposited. These include the depositions in a SiH4–NH3 [1,2] or SiH4–N2 [3] plasma. Additives such as H2 or N2 were also included in SiH4–NH3 plasma [4,5]. For a SiH4–N2 plasma, Ar or He was included [6]. Many process parameters such as radio frequency (rf) power or pressure are typically involved in depositing SiN films. Investigating films characteristics under various plasma conditions demands a prediction model. Unfortunately, constructing a plasma model is very complicated due to the complexity inherent in the plasma process. As a promising means, a neural network in conjunction with a statistical experiment was used to construct a variety of plasma process models [7–13]. Advantages of neural network models over other models include a better prediction, faster response, and easier adaptation to the variations in process parameters or equipment components. In the context of SiN deposition, several neural network models of refractive index [9,12], charge density [9], or life time [9,10] were reported. Among them, the charge density plays an important role in improving the quality of surface passivation. High positive charge density is advantageous from the standpoints of the improved surface passivation of solar cells, a stronger inversion, and a low sheet resistance in metalinsulator-semiconductor inversion solar cells. Unfortunately, charge ⁎ Corresponding author. Tel.: +82 2 3408 3729; fax: +82 2 3408 3329. E-mail address: [email protected] (B. Kim). 0257-8972/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.surfcoat.2008.06.030
density was rarely studied experimentally. The previous model [9] studied very few aspects of charge density. The effects of gas flow rates were little reported as well as their interactions with the substrate temperature. In this study, a charge density model of SiN films was constructed by using a generalized regression neural network (GRNN) [14]. The prediction performance of GRNN model was improved by using a genetic algorithm (GA) [15]. For a systematic modeling, PECVD process was characterized by means of a statistical experimental design. Relationships between charge density and [SiH]/[NH] was identified or validated by using the reported refractive index models. 2. Experimental details The SiN films were deposited by using a Plasma-Therm 700 series batch reactor operating at 13.56 MHz. The distance between the electrodes was 2.29 cm and the electrode diameter was 11 in. The PECVD process was characterized by using a face-centered central composite circumscribed experiment, consisted of 26–1 fractional experiment and 12 axial points [16]. The resulting 32 experiments were used to train the GRNN. Prediction performance of trained GRNN was tested with additional 12 experiments not pertaining to the training data. The ranges of 6 process parameters that were varied in the design were once presented [12]. Four-inch, float zone p-type silicon wafers of (100) orientation with a resistivity of 2.0 Ω-cm were used as the substrate. During the deposition, SiH4 was diluted to 2% in nitrogen. The charge density was measured by using the C–V measurement technique.
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3. Results 3.1. Construction of prediction model A schematic of GRNN is shown in Fig. 1. The input and output of GRNN model consisted of 6 process parameters reported earlier in the work [12] and one charge density, respectively. The prediction performance of GRNN is typically optimized by adjusting the training factor called “spread”. This implies that all spreads in the pattern layer in Fig. 1 are identical. It is expected that better prediction might be achieved by adopting multi-parameterized spreads. A related GA-optimization once applied to model plasma processes [7] was also adopted in building a charge density model here. As the GA parameters, the size of initial population (or solutions) and number of generation were set to the same 100. Each possible solution consisted of 32 spread factors to be optimized. The crossover and mutation probabilities were set to 0.95 and 0.05, respectively. The fitness function was defined in terms of the training error as F¼
1 1 þ RMSEtrain
ð1Þ
where the RMSEtrain was measured with the training data composed of 32 experiments. Here the RMSE represents the root mean square error. A roulette-wheel selection method [15] was used to produce next generations. The prediction performance of GRNN was measured by increasing the spread range from 0.2 to 1.2 with an increment of 0.1. A random generator was used to produce randomized spreads. Prediction errors of GA-trained GRNN models were measured at each spread range. One model of smallest prediction error was then achieved at 0.3 and the corresponding error was 0.72 (×1012/cm2). The residual plot of the optimized model is shown in Fig. 2. As shown in Fig. 2, the errors between the actual and prediction values are very small in nearly all test cases, demonstrating that the model attained a high prediction capability. 3.2. Effect of temperature and NH3 flow rate Fig. 3 shows a charge density as a function of temperature and NH3 flow rate. The pressure, rf power, SiH4 flow rate, and N2 flow rate were set to 0.9 Torr, 30 W, 220 sccm, and 500 sccm, respectively. As shown in Fig. 3, increasing the temperature at 1.0 sccm NH3 flow rate increases the charge density. This is partly supported by the experimental data, which revealed an increase in charge density from 3.72 to 4.8 (×1012/cm2) with increasing the temperature from 200 to 300 °C, respectively. It was reported that increasing the
Fig. 1. A schematic of generalized regression neural network.
Fig. 2. A residual plot of model predictions.
temperature led to a reduction in H concentration [17] as well as a less bonded H [18]. Therefore, the increased charge density at higher temperatures can be attributed to less H. Meanwhile, the refractive index at the same condition exhibited an almost linear increase with the temperature [19]. The expected Si-rich film at higher temperatures implies an increase in [Si–H] compared to [N–H]. In this sense, the charge density variation with the temperature at 1.4 sccm is closely related to [Si–H]/[N–H]. As shown in Fig. 3, the charge density variation with the temperature at 1.4 sccm NH3 flow rate is much different from that noticed at 1.0 sccm. As the temperature increases to less than 250 °C, the charge density is seen to vary little. In contrast, the corresponding refractive index (or [Si–H]) increased [19]. Comparison of these two observations reports a practically useful information that charge density varies little once the increase in [S– H] is not sufficiently large. In particular, the charge density drastically increases with increasing the temperature to more than 250 °C. This implies a presence of a turn over in [Si–H]/[N–H] in those conditions. Fortunately, this was predicted in the refractive index model [19]. Thus, a creation of larger [N–H] than [Si–H] at higher temperature could be ascertained. In consequence, the noticeably increased charge density at higher temperatures is closely correlated to a relatively larger increase in [N–H] than in [Si–H]. In Fig. 3, increasing the NH3 flow rates at 200 °C slightly increases the charge density. A decrease in [Si–H]/[N–H] is therefore expected and this could be ascertained by the refractive index variations [19]. Meanwhile, the increase in charge density becomes more pronounced
Fig. 3. A charge density as a function of temperature and NH3 flow rate.
B. Kim, S.H. Kwon / Surface & Coatings Technology 202 (2008) 5539–5542
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with increasing the NH3 flow rate at a higher temperature of 300 °C. An expected steep decrease in the refractive index could be verified from the refractive index model [19]. Therefore, both charge density and [N–H]/[Si–H] seem to be highly correlated as a function of NH3 flow rate. Another noticeable fact is that charge density is more sensitive to the variation in [N–H] than [Si–H]. 3.3. Effect of temperature and SiH4 flow rate Fig. 4 shows the effect of temperature and SiH4 flow rate on charge density. The pressure, rf power, NH3 flow rate, and N2 flow rate were set to 0.9 Torr, 30 W, 1.2 sccm, and 500 sccm, respectively. Interestingly, the temperature effect on the charge density is little over the entire range of SiH4 flow rates. In contrast, temperature impacts on other deposition rate [20] or refractive index [12] were quite complex depending on SiH4 flow rates. Meanwhile, as shown in Fig. 4, increasing the SiH4 flow rate seems to decrease the charge density. A significant increase in the refractive index occurred for the same variation in SiH4 flow rate [12]. This implies that the charge density is little affected by the increase in [Si–H] as SiH4 flow rate is varied. 3.4. Effect of temperature and rf power Fig. 5 shows a charge density as a function of rf power and temperature. The pressure, NH3 flow rate, SiH4 flow rate, and N2 flow rate were set to 0.9 Torr, 1.2 sccm, 220 sccm, and 500 sccm, respectively. As shown in Fig. 5, depending on the power levels, the temperature effects are much different. Increasing the temperature at 20 W initially varies little the charge density and then considerably increases it. Meanwhile, the refractive index initially increased significantly and then saturated with increasing the temperature at the same rf power [12]. From the model variation, an initial increase in [S–H]/[N–H] and later an enhanced [N–H] are expected. As a result, the latter enhanced charge density can be attributed to relatively large [N–H]. By contrast, the temperature effect at a higher power of 40 W appears to be insignificant. A useful clue to understand this is obtained from the refractive index [12]. The model showed an increase in refractive index with the temperature. This implicates that the film becomes Si-rich due to the relatively larger [Si–H] at elevated temperature. Despite the increased [Si–H], the little varied charge density can be attributed to the weak sensitivity of charge density to the variations in [Si–H] as stated earlier in interpreting Fig. 4. In Fig. 5, increasing the rf power at 250 °C increases the charge density. This indicates that enhancing ion bombardment is beneficial
Fig. 5. A charge density as a function of temperature and rf power.
to increasing charge density. The increase with the rf power is partly supported by the experimental data, which revealed an increase in charge density from 2.27 to 3.12 (×1012/cm2) with increasing the rf power from 20 to 40 W, respectively. In Fig. 5, the increase becomes more conspicuous for the same power at lower temperatures. This implies that ion bombardment effect becomes more effective as the film is likely to contain more H. Meanwhile, the refractive index model [12] predicted a decrease in refractive index with increasing the power at the same temperature. This implies a larger increase in [N–H] than in [Si–H]. The resulting large increase in charge density is clearly shown in Fig. 5. In contrast, as shown in Fig. 5, the charge density appears to slightly decrease with increasing the rf power at a higher temperature of 300 °C. The implicated increase in [Si–H] is certified by the increase in the refractive index [12]. Consequently, as the power was varied, the charge density was strongly dependent on the relative variation in [N–H]/[Si–H]. 4. Conclusion In this study, a neural network model of charge density was constructed by using GRNN and genetic algorithm. Experimental data were collected by means of a statistical experimental design. Optimized model was used to understand parameter effects under various plasma conditions. Particularly, the previous refractive index model was very useful to identify or validate certain relationships between charge density and [N–H]/[Si–H]. Under NH3 flow rate or rf power variation, the temperature was closely correlated to [Si–H]/[N–H]. The power effect was more conspicuous as the film became more N-rich while containing less H. Charge density was more sensitive to [N–H] variation than [Si–H]. All these findings can be effectively utilized to control charge density. Acknowledgements This work was supported by Grant No.R11-2000-086-0000-0 from the Center of Excellency Program of the KOSEF, MOST, and partly by the Ministry of Knowledge Economy, Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Advancement) (IITA-2008C109008010030). References
Fig. 4. A charge density as a function of temperature and SiH4 flow rate.
[1] P. Temple-Boyer, L. Jalabert, L. Masarotto, J.L. Alay, J.R. Morante, J. Vac. Sci. Technol. A. 18 (2000) 2389. [2] Y.B. Park, S.W. Rhee, J. Mat. Sci.: Mat. Elec. 12 (2001) 515.
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[3] C. Doughty, D.C. Knick, J.B. Bailey, J.E. Spencer, J. Vac. Sci. Technol. A. 17 (1999) 2612. [4] H. Gleskova, S. Wagner, V. Gasparik, P. Kovac, Appl. Surf. Sci. 175 (2001) 12. [5] S.E. Aleksandrov, M.L. Khitchman, F.F. Grekov, V.Sh. Ivanov, Russ. J. Appl. Chem. 69 (1996) 1118. [6] J.W. Lee, K.D. Mackenzie, D. Johnson, J.N. Sasserath, S.J. Pearton, F. Ren, J. Electrochem. Soc. 147 (2000) 1481. [7] B. Kim, B.T. Lee, J. Vac. Sci. Technol. A. 22 (2004) 2517. [8] B. Kim, D. Lee, D. Han, J. Kor. Phys. Soc. 46 (2005) 460. [9] S.S. Han, L. Cai, G.S. May, A. Rohatgi, IEEE Trans. Semicond. Manuf. 9 (1996) 303. [10] J.P. Geisler, C.G. George L., G.S. May, IEEE Trans. Semicond. Manuf. 13 (2000) 46. [11] B. Kim, J. Bae, W. Hong, J. Vac. Sci. Technol. A. 23 (2005) 355. [12] B. Kim, S.S. Han, T.S. Kim, B.S. Kim, I.J. Shim, IEEE Trans. Plasma Sci. 31 (2003) 317.
[13] B. Kim, J. Bae, Solid-State Electron. 49 (2005) 1576. [14] D.F. Specht, IEEE Trans. Neural Netw. 2 (1991) 568. [15] D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison Wesley, Reading, MA, 1989. [16] D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Singapore, 1991. [17] B.F. Hanyalogiu, E.S. Aydil, J. Vac. Sci. Technol. A. 16 (1998) 2794. [18] W. Lau, Jpn. J. Appl. Phys. L683 (1990). [19] B. Kim, D.W. Kim, S.S. Han, Vacuum 72 (2004) 385. [20] B. Kim, J.Y. Park, K.K. Lee, J.G. Han, Appl. Surf. Sci. 252 (2006) 4138.
Contents lists available at ScienceDirect
Surface & Coatings Technology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / s u r f c o a t
Temperature effect on charge density of silicon nitride films deposited in SiH4–NH3–N2 plasma Byungwhan Kim ⁎, Sang Hee Kwon Department of Electronic Engineering, Sejong University, 98, Goonja-Dong, Kwangjin-Gu, Seoul, 143-747, Republic of Korea
A R T I C L E
I N F O
Available online 8 June 2008 PACS: 52.75.R 81.15.G Keywords: Silicon nitride film Plasma enhanced chemical vapor deposition Generalized regression neural network Charge density SiH4 Temperature
A B S T R A C T Improved surface passivation of silicon nitride films requires a high positive charge density. A neural network model of SiN charge density was used to investigate temperature effects on charge density. For a systematic modeling, the deposition process was characterized by means of a face-centered Box Wilson experiment. Prediction performance of neural network model was optimized by using genetic algorithm. Interestingly, charge density was varied little with the substrate temperature regardless of SiH4 flow rates. Charge density variation was not sensitive to [Si–H] variation. A pronounced temperature effect at higher NH3 flow rate or lower radio frequency (rf) power was attributed to a relatively large [N–H]. For NH3 or rf power variation, charge density was strongly correlated to [N–H]/[Si–H]. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Silicon nitride (SiN) films are widely used as a passivation layer in fabricating solar cells. Using a plasma-enhanced chemical vapor deposition (PECVD) system, various SiN films were deposited. These include the depositions in a SiH4–NH3 [1,2] or SiH4–N2 [3] plasma. Additives such as H2 or N2 were also included in SiH4–NH3 plasma [4,5]. For a SiH4–N2 plasma, Ar or He was included [6]. Many process parameters such as radio frequency (rf) power or pressure are typically involved in depositing SiN films. Investigating films characteristics under various plasma conditions demands a prediction model. Unfortunately, constructing a plasma model is very complicated due to the complexity inherent in the plasma process. As a promising means, a neural network in conjunction with a statistical experiment was used to construct a variety of plasma process models [7–13]. Advantages of neural network models over other models include a better prediction, faster response, and easier adaptation to the variations in process parameters or equipment components. In the context of SiN deposition, several neural network models of refractive index [9,12], charge density [9], or life time [9,10] were reported. Among them, the charge density plays an important role in improving the quality of surface passivation. High positive charge density is advantageous from the standpoints of the improved surface passivation of solar cells, a stronger inversion, and a low sheet resistance in metalinsulator-semiconductor inversion solar cells. Unfortunately, charge ⁎ Corresponding author. Tel.: +82 2 3408 3729; fax: +82 2 3408 3329. E-mail address: [email protected] (B. Kim). 0257-8972/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.surfcoat.2008.06.030
density was rarely studied experimentally. The previous model [9] studied very few aspects of charge density. The effects of gas flow rates were little reported as well as their interactions with the substrate temperature. In this study, a charge density model of SiN films was constructed by using a generalized regression neural network (GRNN) [14]. The prediction performance of GRNN model was improved by using a genetic algorithm (GA) [15]. For a systematic modeling, PECVD process was characterized by means of a statistical experimental design. Relationships between charge density and [SiH]/[NH] was identified or validated by using the reported refractive index models. 2. Experimental details The SiN films were deposited by using a Plasma-Therm 700 series batch reactor operating at 13.56 MHz. The distance between the electrodes was 2.29 cm and the electrode diameter was 11 in. The PECVD process was characterized by using a face-centered central composite circumscribed experiment, consisted of 26–1 fractional experiment and 12 axial points [16]. The resulting 32 experiments were used to train the GRNN. Prediction performance of trained GRNN was tested with additional 12 experiments not pertaining to the training data. The ranges of 6 process parameters that were varied in the design were once presented [12]. Four-inch, float zone p-type silicon wafers of (100) orientation with a resistivity of 2.0 Ω-cm were used as the substrate. During the deposition, SiH4 was diluted to 2% in nitrogen. The charge density was measured by using the C–V measurement technique.
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3. Results 3.1. Construction of prediction model A schematic of GRNN is shown in Fig. 1. The input and output of GRNN model consisted of 6 process parameters reported earlier in the work [12] and one charge density, respectively. The prediction performance of GRNN is typically optimized by adjusting the training factor called “spread”. This implies that all spreads in the pattern layer in Fig. 1 are identical. It is expected that better prediction might be achieved by adopting multi-parameterized spreads. A related GA-optimization once applied to model plasma processes [7] was also adopted in building a charge density model here. As the GA parameters, the size of initial population (or solutions) and number of generation were set to the same 100. Each possible solution consisted of 32 spread factors to be optimized. The crossover and mutation probabilities were set to 0.95 and 0.05, respectively. The fitness function was defined in terms of the training error as F¼
1 1 þ RMSEtrain
ð1Þ
where the RMSEtrain was measured with the training data composed of 32 experiments. Here the RMSE represents the root mean square error. A roulette-wheel selection method [15] was used to produce next generations. The prediction performance of GRNN was measured by increasing the spread range from 0.2 to 1.2 with an increment of 0.1. A random generator was used to produce randomized spreads. Prediction errors of GA-trained GRNN models were measured at each spread range. One model of smallest prediction error was then achieved at 0.3 and the corresponding error was 0.72 (×1012/cm2). The residual plot of the optimized model is shown in Fig. 2. As shown in Fig. 2, the errors between the actual and prediction values are very small in nearly all test cases, demonstrating that the model attained a high prediction capability. 3.2. Effect of temperature and NH3 flow rate Fig. 3 shows a charge density as a function of temperature and NH3 flow rate. The pressure, rf power, SiH4 flow rate, and N2 flow rate were set to 0.9 Torr, 30 W, 220 sccm, and 500 sccm, respectively. As shown in Fig. 3, increasing the temperature at 1.0 sccm NH3 flow rate increases the charge density. This is partly supported by the experimental data, which revealed an increase in charge density from 3.72 to 4.8 (×1012/cm2) with increasing the temperature from 200 to 300 °C, respectively. It was reported that increasing the
Fig. 1. A schematic of generalized regression neural network.
Fig. 2. A residual plot of model predictions.
temperature led to a reduction in H concentration [17] as well as a less bonded H [18]. Therefore, the increased charge density at higher temperatures can be attributed to less H. Meanwhile, the refractive index at the same condition exhibited an almost linear increase with the temperature [19]. The expected Si-rich film at higher temperatures implies an increase in [Si–H] compared to [N–H]. In this sense, the charge density variation with the temperature at 1.4 sccm is closely related to [Si–H]/[N–H]. As shown in Fig. 3, the charge density variation with the temperature at 1.4 sccm NH3 flow rate is much different from that noticed at 1.0 sccm. As the temperature increases to less than 250 °C, the charge density is seen to vary little. In contrast, the corresponding refractive index (or [Si–H]) increased [19]. Comparison of these two observations reports a practically useful information that charge density varies little once the increase in [S– H] is not sufficiently large. In particular, the charge density drastically increases with increasing the temperature to more than 250 °C. This implies a presence of a turn over in [Si–H]/[N–H] in those conditions. Fortunately, this was predicted in the refractive index model [19]. Thus, a creation of larger [N–H] than [Si–H] at higher temperature could be ascertained. In consequence, the noticeably increased charge density at higher temperatures is closely correlated to a relatively larger increase in [N–H] than in [Si–H]. In Fig. 3, increasing the NH3 flow rates at 200 °C slightly increases the charge density. A decrease in [Si–H]/[N–H] is therefore expected and this could be ascertained by the refractive index variations [19]. Meanwhile, the increase in charge density becomes more pronounced
Fig. 3. A charge density as a function of temperature and NH3 flow rate.
B. Kim, S.H. Kwon / Surface & Coatings Technology 202 (2008) 5539–5542
5541
with increasing the NH3 flow rate at a higher temperature of 300 °C. An expected steep decrease in the refractive index could be verified from the refractive index model [19]. Therefore, both charge density and [N–H]/[Si–H] seem to be highly correlated as a function of NH3 flow rate. Another noticeable fact is that charge density is more sensitive to the variation in [N–H] than [Si–H]. 3.3. Effect of temperature and SiH4 flow rate Fig. 4 shows the effect of temperature and SiH4 flow rate on charge density. The pressure, rf power, NH3 flow rate, and N2 flow rate were set to 0.9 Torr, 30 W, 1.2 sccm, and 500 sccm, respectively. Interestingly, the temperature effect on the charge density is little over the entire range of SiH4 flow rates. In contrast, temperature impacts on other deposition rate [20] or refractive index [12] were quite complex depending on SiH4 flow rates. Meanwhile, as shown in Fig. 4, increasing the SiH4 flow rate seems to decrease the charge density. A significant increase in the refractive index occurred for the same variation in SiH4 flow rate [12]. This implies that the charge density is little affected by the increase in [Si–H] as SiH4 flow rate is varied. 3.4. Effect of temperature and rf power Fig. 5 shows a charge density as a function of rf power and temperature. The pressure, NH3 flow rate, SiH4 flow rate, and N2 flow rate were set to 0.9 Torr, 1.2 sccm, 220 sccm, and 500 sccm, respectively. As shown in Fig. 5, depending on the power levels, the temperature effects are much different. Increasing the temperature at 20 W initially varies little the charge density and then considerably increases it. Meanwhile, the refractive index initially increased significantly and then saturated with increasing the temperature at the same rf power [12]. From the model variation, an initial increase in [S–H]/[N–H] and later an enhanced [N–H] are expected. As a result, the latter enhanced charge density can be attributed to relatively large [N–H]. By contrast, the temperature effect at a higher power of 40 W appears to be insignificant. A useful clue to understand this is obtained from the refractive index [12]. The model showed an increase in refractive index with the temperature. This implicates that the film becomes Si-rich due to the relatively larger [Si–H] at elevated temperature. Despite the increased [Si–H], the little varied charge density can be attributed to the weak sensitivity of charge density to the variations in [Si–H] as stated earlier in interpreting Fig. 4. In Fig. 5, increasing the rf power at 250 °C increases the charge density. This indicates that enhancing ion bombardment is beneficial
Fig. 5. A charge density as a function of temperature and rf power.
to increasing charge density. The increase with the rf power is partly supported by the experimental data, which revealed an increase in charge density from 2.27 to 3.12 (×1012/cm2) with increasing the rf power from 20 to 40 W, respectively. In Fig. 5, the increase becomes more conspicuous for the same power at lower temperatures. This implies that ion bombardment effect becomes more effective as the film is likely to contain more H. Meanwhile, the refractive index model [12] predicted a decrease in refractive index with increasing the power at the same temperature. This implies a larger increase in [N–H] than in [Si–H]. The resulting large increase in charge density is clearly shown in Fig. 5. In contrast, as shown in Fig. 5, the charge density appears to slightly decrease with increasing the rf power at a higher temperature of 300 °C. The implicated increase in [Si–H] is certified by the increase in the refractive index [12]. Consequently, as the power was varied, the charge density was strongly dependent on the relative variation in [N–H]/[Si–H]. 4. Conclusion In this study, a neural network model of charge density was constructed by using GRNN and genetic algorithm. Experimental data were collected by means of a statistical experimental design. Optimized model was used to understand parameter effects under various plasma conditions. Particularly, the previous refractive index model was very useful to identify or validate certain relationships between charge density and [N–H]/[Si–H]. Under NH3 flow rate or rf power variation, the temperature was closely correlated to [Si–H]/[N–H]. The power effect was more conspicuous as the film became more N-rich while containing less H. Charge density was more sensitive to [N–H] variation than [Si–H]. All these findings can be effectively utilized to control charge density. Acknowledgements This work was supported by Grant No.R11-2000-086-0000-0 from the Center of Excellency Program of the KOSEF, MOST, and partly by the Ministry of Knowledge Economy, Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Advancement) (IITA-2008C109008010030). References
Fig. 4. A charge density as a function of temperature and SiH4 flow rate.
[1] P. Temple-Boyer, L. Jalabert, L. Masarotto, J.L. Alay, J.R. Morante, J. Vac. Sci. Technol. A. 18 (2000) 2389. [2] Y.B. Park, S.W. Rhee, J. Mat. Sci.: Mat. Elec. 12 (2001) 515.
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B. Kim, S.H. Kwon / Surface & Coatings Technology 202 (2008) 5539–5542
[3] C. Doughty, D.C. Knick, J.B. Bailey, J.E. Spencer, J. Vac. Sci. Technol. A. 17 (1999) 2612. [4] H. Gleskova, S. Wagner, V. Gasparik, P. Kovac, Appl. Surf. Sci. 175 (2001) 12. [5] S.E. Aleksandrov, M.L. Khitchman, F.F. Grekov, V.Sh. Ivanov, Russ. J. Appl. Chem. 69 (1996) 1118. [6] J.W. Lee, K.D. Mackenzie, D. Johnson, J.N. Sasserath, S.J. Pearton, F. Ren, J. Electrochem. Soc. 147 (2000) 1481. [7] B. Kim, B.T. Lee, J. Vac. Sci. Technol. A. 22 (2004) 2517. [8] B. Kim, D. Lee, D. Han, J. Kor. Phys. Soc. 46 (2005) 460. [9] S.S. Han, L. Cai, G.S. May, A. Rohatgi, IEEE Trans. Semicond. Manuf. 9 (1996) 303. [10] J.P. Geisler, C.G. George L., G.S. May, IEEE Trans. Semicond. Manuf. 13 (2000) 46. [11] B. Kim, J. Bae, W. Hong, J. Vac. Sci. Technol. A. 23 (2005) 355. [12] B. Kim, S.S. Han, T.S. Kim, B.S. Kim, I.J. Shim, IEEE Trans. Plasma Sci. 31 (2003) 317.
[13] B. Kim, J. Bae, Solid-State Electron. 49 (2005) 1576. [14] D.F. Specht, IEEE Trans. Neural Netw. 2 (1991) 568. [15] D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison Wesley, Reading, MA, 1989. [16] D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Singapore, 1991. [17] B.F. Hanyalogiu, E.S. Aydil, J. Vac. Sci. Technol. A. 16 (1998) 2794. [18] W. Lau, Jpn. J. Appl. Phys. L683 (1990). [19] B. Kim, D.W. Kim, S.S. Han, Vacuum 72 (2004) 385. [20] B. Kim, J.Y. Park, K.K. Lee, J.G. Han, Appl. Surf. Sci. 252 (2006) 4138.