High‐aspect ratio contact (HARC) etching is a bottleneck step of the high‐definition organic light emitting diode (OLED) display manufacturing processes. HARC process is frequently failed during the mass production, because this requires the high‐energy ion flux and the sidewall passivation, simultaneously. To analyze the cause of HARC process failures, plasma information (PI)‐based virtual metrology (VM) algorithm was developed by using the equipment engineering system and the optical emission spectroscopy data recorded from the fab. Developed PI‐VM could predict the process faults with >90% of the accuracy, and the cause analysis function was also validated. We could suggest a right solution to the failure, and more efficient management of the OLED display manufacturing was possible.

Metal target dry etching process applied for the organic light emitting diode display manufacturing is hard to control without the generation of the defect particles. A large amount of the metal-halide by-prodcucts with the non-volatile physical nature are produced in the large area plasma-assisted process chamber. To achieve high-density plasma-based throughput, the inductively coupled plasma type dry etchers were adopted for large-area display manufacturing processes. However, this type of plasma source causes the ion flux-driven damages on the chamber inner walls near the RF power supplied antenna. Sputtered Al atoms from the ceramic parts or etching targets were redeposited onto the chamber inner walls after they form the metal-halide compounds. Redeposited by-prodcucts have very high binding energies to decompose. Undecomposed layers were stuck on the chamber inner wall and flaked off later to form the defect particles. To control this undesired phenomenon, decomposition reaction activated—and plasma locality controlled—two types of ISDs (In Situ Dry cleanings) were designed. A more appropriate type of ISD had selected referring to the developed PI-VM (Plasma Information based Virtual Metrology) model, which qualifies the start of mass production after the discontinuities of the process. The big data set of equipment engineering system and optical emission spectroscopy, accumulated during the mass production, were parameterized to the PI parameters and were applied to the PI-VM modeling. Management of the mass production with the designed ISD and PI-VM model could reduce the 25% of defect particle driven yield loss.

Generation of the defect particles during the plasma-assisted metal dry etching process is induced by the various mechanisms. Most of these mechanisms are caused by the non-volatility of metalhalide compounds generated during the etching process. Degeneration of the metal etching process chamber condition is observed as the frequent process fault caused by the defects, but the worse condition is not recovered by itself. Because of this property of the metal etching process, proper work of the preventive maintenance (PM) to restore the process chamber or the addition of a discharge cleaning step is required periodically. However, inadequate PM or discharge cleaning by the uncertain cause analysis of the defect generation should be a just temporary remedy, and might lead the repetition of similar problems. To solve this problem, the virtual metrology (VM) model based on the plasma information (PI) parameters, known as PI-VM, was developed and applied for the defect control of the metal layer dry etching processes in organic light emitting diode (OLED) display manufacturing. To obtain the information about the generation rates of non-volatile compounds and their removal rates by the exhaustion system, PI parameters are designed with the consideration of the reaction kinetics in the metal etching plasma volume, sheath, and reacting surface using the big data of equipment engineering system (EES) and optical emission spectroscopy (OES) accumulated during the mass production process. The developed PI-VM index could be applied to a 2-3 h earlier alarm system for the defect occurrence, and had succeeded over 90% of alarm rate. This PI-VM alarm was applied to predictive control of the process by the early substitution of the discharge cleaning step or by the repair of the proper parts in the process chamber. By the control of processes based on the predicted results of the PI-VM, management of the mass production line with about 30% decreased defect was possible in OLED display manufacturing.