Data-driven plasma science-based plasma etching process design in OLED and Semiconductor mass productions referring to PI-VM

In this presentation, we review the development of plasma engineering technology that dramatically improves the production efficiency of OLED (organic light-emitting diode) displays and semiconductor manufacturing by utilizing a VM(Virtual Metrology) methodology based on physical domain knowledge. The domain knowledge consists of plasma-heating and sheath physics, plasma chemistry and plasma-material surface reaction kinetics, and plasma diagnostics. Based on this, a plasma information-based virtual metrology (PI-VM) algorithm was developed and drastically enhanced process prediction performance by parameterizing plasma information (PI), which can trace the states of processing plasma. PI-VM has superior process prediction accuracy as an application of data-driven plasma science compared to classical statistics-based virtual metrologies. The developed PI-VM algorithms adopted for practical processing issues such as the control and design of the OLED-display mass production: the process fault prediction and root cause analysis of mass-producing etching process, micro-uniformity problems in the 6.0G large-area target processing, and the plant design for 8.6G super large-area target mass production fab [1,2]. They demonstrated savings of approximately 25% of the yield loss over the past 8 years. Recently, PI-VM has played an important role in the development of next-generation plasma processes for the manufacturing of highly functional advanced OLED displays [1]. This improvement was achieved with the development of FDC (fault detection and classification) and APC (advanced process control) logic, which can be developed through the analysis of the physical characteristics of the feature parameters used in PI-VM with the evaluation of their contributions and their correlations to the processing results. PI-VM provides leverage that can be applied in the development of process equipment and factory automation technologies.  

[1] S. Park, J.Seong, G.-H. Kim et al., J. Kor. Phys. Soc., 80 (2022) 8.

[2] S. Park, J.Seong, Y.Park, Y.Noh, H.Lee, N.Bae, K.-B.Roh, R.Seo, B.Song, and G.-H.Kim, Plasma.Phys.Cont.Fusion. 66 (2024) 2.