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Numerical Experiment Using Direct Simulation Monte Carlo for Improving Material Deposition Uniformity During OLED Manufacturing

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Abstract

Numerical experiments to improve the deposition uniformity in organic light-emitting diode (OLED) manufacturing process using direct simulation Monte Carlo (DSMC) technique running on general-purpose graphical processing unit (GPGPU) architecture were conducted in this study. Two ways to improve the deposition uniformity are proposed: one is to select a combination of different nozzle cap diameters, thereby controlling the amount and distribution of evaporated organic material, and the other is to investigate the effect of installing two inner plates on the change in the deposition uniformity associated with the transport of organic material within the crucible. For this purpose, some rules must be followed, namely, different diameters of nozzle caps, e.g., 8, 9 and 10 mm, must be used, and eight nozzles at both edges of the nozzle array must have a diameter of 10 mm. DSMC simulations were performed for all cases with new sets of nozzle array by replacing the initially installed 10 mm diameter caps with 8 or 9 mm diameters caps sequentially. From DSMC calculations of the corresponding deposition uniformity values, it was found that 8 mm diameter caps are more effective than 9 mm diameter caps in producing a good quality of deposition on the glass panel, and the final combination of nozzle caps that satisfies the target uniformity value of less than 2% is finally obtained. On removing the inner plate located immediately below the nozzle array, the deposition uniformity became considerably worse, clearly indicating that the control of the evaporated material transport influences the whole quality of the deposition thickness on the glass panel. It is thus verified that the amount of ejected material through nozzles that are located at the center of nozzle array, and the type of inner plate located below the nozzle, must be precisely determined for manufacturing high-quality OLED glass panels. This study specifically presents the process and possibility of a scientific simulation of DSMC dealing with the behavior of rarefied gases to achieve the optimized goal of a high quality of deposition uniformity in the practical manufacturing process.

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Abbreviations

\({C}_{\mathrm{m}\mathrm{a}\mathrm{x}}\) :

Maximum thickness of deposited material

\({C}_{\mathrm{m}\mathrm{i}\mathrm{n}}\) :

Minimum thickness of deposited material

\({C}_{\mathrm{u}\mathrm{n}\mathrm{i}\mathrm{f}}\) :

Deposition uniformity

\({c}_{r}\) :

Relative speed

\({d}_{\mathrm{r}\mathrm{e}\mathrm{f}}\) :

Reference diameter

\({F}_{N}\) :

Ratio of the number of real molecules to simulated particles

\({k}_{B}\) :

Boltzmann constant

\({m}_{r}\) :

Reduced mass

\(N\) :

Number of simulated particles

\({N}_{\mathrm{c}\mathrm{o}\mathrm{l}}\) :

Number of collision pairs

\(n\) :

Number of real molecules

\({P}_{c}\) :

Collision probability

\({T}_{\mathrm{r}\mathrm{e}\mathrm{f}}\) :

Reference temperature

\(t\) :

Time

\({V}_{c}\) :

Cell volume

\({\sigma }_{r}\) :

Collisional cross section

\(\omega\) :

Coefficient of viscosity

References

  1. Choi, H., Shin, S., & Jeon, H. (2016). Fast spatial atomic layer deposition of Al2O3 at low temperature (< 100C) as a gas permeation barrier for flexible organic light-emitting diode displays. Journal of Vacuum Science & Technology A, 34(1), 01A121.

    Article  Google Scholar 

  2. Mi, B. X., Gao, Z. Q., Liu, M. W., Chan, K. Y., Kwong, H. L., Wong, N. B., Lee, C. S., Hung, L. S., & Lee, S. T. (2002). New polycyclic aromatic hydrocarbon dopants for red organic electroluminescent devices”. Journal of Materials Chemistry, 12(5), 1307–1310.

    Article  Google Scholar 

  3. Ma, C., Zhang, B., Liang, Z., Xie, P., Wang, X., Zhang, B., Cao, Y., Jiang, X., & Zhang, Z. (2002). A novel n-type red luminescent material for organic light-emitting diodes. Journal of Materials Chemistry, 12(6), 1671–1675.

    Article  Google Scholar 

  4. Arnold, M. S., McGraw, G. J., Forrest, S. R., & Lunt, R. R. (2008). Direct vapor jet printing of three color segment organic light emitting device for white light illumination. Applied Physics Letters, 92(5), 053301.

    Article  Google Scholar 

  5. Eritt, M., May, C., Leo, K., Toerker, M., & Radehaus, C. (2010). OLED manufacturing for large area lighting applications. Thin Solid Films, 518(11), 3042–3045.

    Article  Google Scholar 

  6. Lee, J., Park, S., Park, J. C., & Shin, Y.-S. (2015). Analysis of adhesion strength of laminated copper layers in roll-to-roll lamination process. International Journal of Precision Engineering & Manufacturing, 16(9), 2013–2020.

    Article  Google Scholar 

  7. Kim, S.-H., Lee, M.-Y., Woo, K., Youn, H., Lee, T.-M., Lee, E.-K., & Kwon, S. (2017). A study on thin film uniformity in a roll-to-roll thermal evaporation system for flexible OLED applications. International Journal of Precision Engineering & Manufacturing, 18(8), 1111–1117.

    Article  Google Scholar 

  8. Elsing, R. (1991). The practical use and application of Monte-Carlo studies in physical vapour deposition technology. Surface and Coatings Technology, 49(1–3), 132–138.

    Article  Google Scholar 

  9. Motohiro, T., & Taga, Y. (1984). Monte Carlo simulation of the particle transport process in sputter deposition. Thin Solid Films, 112(2), 161–173.

    Article  Google Scholar 

  10. Sasajina, Y., Tsukida, T., Ozawa, S., & Yamamoto, R. (1992). Monte-Carlo simulation study of the vacuum deposition process. Applied Surface Science, 60–61, 653–659.

    Article  Google Scholar 

  11. Battaile, C., (2005) “Monte Carlo Methods for Simulating Thin Film Deposition,” in Handbook of Materials Modeling, edited by S. Yip (Springer), 2363–2377.

  12. Sohn, I., Kim, J., Bae, J., & Lee, J. (2016). Efficiency enhancement of PIC-MCC modeling using GPU parallelization. IEEE Transactions on Plasma Science, 44(9), 1823–1833.

    Article  Google Scholar 

  13. Xiao, H., Shi, Y.-Y., Xu, Z.-Z., Li, D.-Y., & Lyu, S.-K. (2015). Study on flow and heat transfer of small scale gas flow for air cooling system. International Journal of Precision Engineering & Manufacturing, 16(12), 2491–2498.

    Article  Google Scholar 

  14. Bird, G. A. (1994). Molecular Gas Dynamics and the Direct Simulation of Gas Flows. New York: Oxford University Press.

    Google Scholar 

  15. Kim, J., & Sohn, I. (2020). Simplified modeling of charged gas particles coupled with electromagnetic field using Monte Carlo and Finite Volume Method. IEEE Transactions on Plasma Science, 48(4), 921–929.

    Article  Google Scholar 

  16. Sohn, I., Seo, I., Lee, S., & Jeong, S. (2019). Improved modeling of material deposition during OLED manufacturing using direct simulation Monte Carlo method on GPU architecture. International Journal of Precision Engineering and Manufacturing-Green Technology, 6(5), 861–873.

    Article  Google Scholar 

  17. Lee, E. K. (2009). Three dimensional direct Monte Carlo simulation on OLED evaporation process. Journal of the Semiconductor & Display Equipment Technology, 8(4), 37–42.

    Google Scholar 

  18. Lee, E. K. (2009). Simulation of the thin-film thickness distribution for an OLED thermal evaporation process. Vacuum, 83(5), 848–852.

    Article  Google Scholar 

  19. Lee, E. K. (2016). Practical point source positioning for an OLED deposition system. Journal of Nanoelectronics and Optoelectronics, 11(2), 148–152.

    Article  Google Scholar 

  20. Scanlon, T. J., White, C., Borg, M. K., Palharini, R. C., Farbau, E., Boyd, I. D., Reesetf, J. M., & Browntt, R. E. (2015). Open-source direct simulation Monte Carlo chemistry modeling for hypersonic flows. AIAA Journal., 53(4), 1670–1680.

    Article  Google Scholar 

  21. Scanlon, T. J., Roohi, E., White, C., Darbandi, M., & Reese, J. M. (2010). An open source, parallel DSMC code for rarefied gas flows in arbitrary geometries. Computers & Fluids, 39(10), 2078–2089.

    Article  Google Scholar 

  22. Palharini, R. C., White, C., Scanlon, T. J., Brown, R. E., Borg, M. K., & Reese, J. M. (2015). Benchmark numerical simulations of rarefied non-reacting gas flows using an open-source DSMC code. Computers & Fluids, 120, 140–157.

    Article  MathSciNet  Google Scholar 

  23. Shang, Z., & Chen, S. (2013). 3D DSMC simulation of rarefied gas flows around a space crew capsule using OpenFOAM. Open Journal of Applied Sciences, 3(1), 35–38.

    Article  Google Scholar 

  24. Ivanov, M. S., & Gimelshein, S. F. (1998). Computational hypersonic rarefied flows. Annual Review of Fluid Mechanics, 30, 469–505.

    Article  MathSciNet  Google Scholar 

  25. Sohn, I., Li, Z., Levin, D. A., & Modest, M. F. (2012). Coupled DSMC-PMC radiation simulations of a hypersonic reentry. Journal of Thermophysics and Heat Transfer, 26(1), 22–35.

    Article  Google Scholar 

  26. Xiao, H., Tang, K., Xu, Z.-Z., Li, D.-Y., & Lyu, S.-K. (2016). Numerical study of shock/vortex interaction in diatomic gas flows. International Journal of Precision Engineering and Manufacturing, 17(1), 27–34.

    Article  Google Scholar 

  27. Gavasane, A., Agrawal, A., & Bhandarkar, U. (2018). Study of rarefied gas flows in backward facing micro-step using direct simulation Monte Carlo. Vacuum, 155, 249–259.

    Article  Google Scholar 

  28. White, C., Borg, M. K., Scanlon, T. J., & Reese, J. M. (2013). A DSMC investigation of gas flows in micro-channels with bends. Computers & Fluids, 71, 261–271.

    Article  MathSciNet  Google Scholar 

  29. Piekos, E. S., & Breuer, K. S. (1996). Numerical modeling of micromechanical devices using the direct simulation Monte Carlo method. Journal of Fluids Engineering, 118(3), 464–469.

    Article  Google Scholar 

  30. Oran, E. S., Oh, C. K., & Cybyk, B. Z. (1998). Direct simulation Monte Carlo: Recent advances and applications. Annual Review of Fluid Mechanics, 30(1), 403–441.

    Article  MathSciNet  Google Scholar 

  31. Moss, J. N., Mitcheltree, R. A., Dogra, V. K., & Wilmoth, R. G. (1994). Direct simulation Monte Carlo and Navier-Stokes simulations of blunt body wake flows. AIAA Journal, 32(7), 1399–1406.

    Article  Google Scholar 

  32. Schwartzentruber, T. E., & Boyd, I. D. (2006). A hybrid particle-continuum method applied to shock waves. Journal of Computational Physics, 215(2), 402–416.

    Article  MathSciNet  Google Scholar 

  33. Nizenkov, P., Pfeiffer, M., Mirza, A., & Fasoulas, S. (2017). Modeling of chemical reactions between polyatomic molecules for atmospheric entry simulations with direct simulation Monte Carlo. Physics of Fluids, 29(7), 077104.

    Article  Google Scholar 

  34. Gimelshein, S. F., & Wysong, I. J. (2019). Bird’s total collision energy model: 4 decades and going strong. Physics of Fluids, 31(7), 076101.

    Article  Google Scholar 

  35. Bird, G. A. (2011). The Q-K model for gas-phase chemical reaction rates. Physics of Fluids, 23(10), 106101.

    Article  Google Scholar 

  36. Liechty, D. S., & Lewis, M. J. (2014). Extension of the Quantum-Kinetic Model to Lunar and Mars Return Physics. Physics of Fluids, 26(2), 27106.

    Article  Google Scholar 

  37. Gallis, M. A., Bond, R. B., & Torczynski, J. R. (2009). A kinetic-theory approach for computing chemical-reaction rates in upper-atmosphere hypersonic flows. Journal of Chemical Physics, 131, 1–13.

    Article  Google Scholar 

Download references

Acknowledgements

This project was financially supported by the institutional research program (No. K20L04C03) of Korea Institute of Science and Technology Information (KISTI), Republic of Korea, 2020.

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Authors and Affiliations

  1. Korea Institute of Science and Technology Information, Daejeon, Republic of Korea

    Ilyoup Sohn

  2. Metariver Technology Co., Ltd, Seoul, Republic of Korea

    Insoo Seo, Sanghyun Lee & Sean Jeong

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  1. Ilyoup Sohn
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Correspondence to Ilyoup Sohn.

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Sohn, I., Seo, I., Lee, S. et al. Numerical Experiment Using Direct Simulation Monte Carlo for Improving Material Deposition Uniformity During OLED Manufacturing. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 1049–1062 (2022). https://doi.org/10.1007/s40684-021-00370-3

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