반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

무작위 생성 심층신경망 기반 유기발광다이오드 흑점 성장가속 전산모사를 통한 소자 변수 추출

Extraction of the OLED Device Parameter based on Randomly Generated Monte Carlo Simulation with Deep Learning

  • 유승열 (고려대학교 공과대학 전기전자공학과) ;
  • 박일후 (고려대학교 공과대학 전기전자공학과) ;
  • 김규태 (고려대학교 공과대학 전기전자공학과)
  • You, Seung Yeol (School of Electrical Engineering, Korea University) ;
  • Park, Il-Hoo (School of Electrical Engineering, Korea University) ;
  • Kim, Gyu-Tae (School of Electrical Engineering, Korea University)
  • 투고 : 2021.09.03
  • 심사 : 2021.09.16
  • 발행 : 2021.09.30
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초록

Numbers of studies related to optimization of design of organic light emitting diodes(OLED) through machine learning are increasing. We propose the generative method of the image to assess the performance of the device combining with machine learning technique. Principle parameter regarding dark spot growth mechanism of the OLED can be the key factor to determine the long-time performance. Captured images from actual device and randomly generated images at specific time and initial pinhole state are fed into the deep neural network system. The simulation reinforced by the machine learning technique can predict the device parameters accurately and faster. Similarly, the inverse design using multiple layer perceptron(MLP) system can infer the initial degradation factors at manufacturing with given device parameter to feedback the design of manufacturing process.

키워드

과제정보

This work was supported by Samsung Display Co. Ltd.

참고문헌

  1. OKADA, Takeru; YOSHIDA, Ayako; TSUJI, Taishi., "Dark spot growth and its acceleration factor in organic light-emitting diodes with single barrier structure," Japanese Journal of Applied Physics, 56.6: 060305, 2017. https://doi.org/10.7567/JJAP.56.060305
  2. Visweswaran, B., Mandlik, P., Mohan, S. H., Silvernail, J. A., Ma, R., Sturm, J. C., & Wagner, S., "Diffusion of water into permeation barrier layers," Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 33(3), 031513, 2015. https://doi.org/10.1116/1.4918327
  3. Swayamprabha, S. S., Dubey, D. K., Shahnawaz, R. A. K. Y., Nagar, M. R., Sharma, A., Tung, F. C., & Jou, J. H., "Approaches for long lifetime organic light emitting diodes," Advanced Science, 8(1), 2020.
  4. AZRAIN, M. M., et al., "Analysis of mechanisms responsible for the formation of dark spots in organic light emitting diodes (OLEDs): A review," Synthetic Metals, 235, pp. 160-175, 2018. https://doi.org/10.1016/j.synthmet.2017.12.011
  5. Islam, A., Rabbani, M., Bappy, M. H., Miah, M. A. R., & Sakib, N., "A review on fabrication process of organic light emitting diodes," In 2013 International Conference on Informatics, Electronics and Vision (ICIEV), IEEE, pp. 1-5, 2013.
  6. Saeki, A., & Kranthiraja, K., "A high throughput molecular screening for organic electronics via machine learning: present status and perspective," Japanese Journal of Applied Physics, 59(SD), SD0801, 2019. https://doi.org/10.7567/1347-4065/ab4f39
  7. Phatak, R., Tsui, T. Y., & Aziz, H., "Dependence of dark spot growth on cathode/organic interfacial adhesion in organic light emitting devices," Journal of Applied Physics, 111(5), 054512, 2012. https://doi.org/10.1063/1.3692390
  8. Geffroy, B., Le Roy, P., & Prat, C., "Organic light-emitting diode (OLED) technology: materials, devices and display technologies," Polymer international, 55(6), pp. 572-582, 2006. https://doi.org/10.1002/pi.1974
  9. Wang, Z., Gao, J., Jian, C., Cen, Y., & Chen, X., "OLED defect inspection system development through independent component analysis," TELKOMNIKA Indonesian Journal of Electrical Engineering, 10(8), pp. 2309-2319, 2012.
  10. Liew, Y. F., Zhu, F., & Chua, S. J., "Effect of organic layer combination on dark spot formation in organic light emitting devices," Chemical physics letters, 394(4-6), pp. 275-279, 2004. https://doi.org/10.1016/j.cplett.2004.07.023