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

Abstract

Abstract The production efficiencies of organic light emitting diode (OLED) displays and semiconductor manufacturing have been dramatically improving with the help of plasma physics and engineering technology by utilizing a process monitoring methodology based on physical domain knowledge. This domain knowledge consists of plasma-heating and sheath physics, plasma chemistry, and plasma-material surface reaction kinetics. They were applied to the plasma information based virtual metrology (PI-VM) algorithm with the plasma diagnostics and noticeably enhanced process prediction performance by parameterizing plasma information (PI) in various processes of OLED display and semiconductor manufacturing fabs. PI-VM has shown superior process prediction accuracy, which can trace the states of processing plasmas as an application of data-driven plasma science compared to the classical statistics and machine learning-based virtual metrologies; thus, various plasma processes have been managed and controlled with the help of the PI-VM models. More than this, we have adopted the PI-VM model to optimize the patterning architecture and plasma processes simultaneously. The best combination of the etching pattern structure and plasma condition was adjustable based on the detailed understanding of the angular distribution of sputtered atoms from the etching target surface and their interaction with the plasma sheath based on the PI-VM modeling for etching profile failure prediction.