Application of PI-VM for management of the metal target plasma etching processes in OLED display manufacturing

Abstract

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.

Cause analysis of the faults in HARC etching processes by using the PI‐VM model for OLED display manufacturing

“…EEDF is the representative plasma property about the thermal equilibrium state, and governs the various reactions in the plasma as represented in Eq. . To monitor the variation of EEDF tail shape effectively, b ‐factor, the shape factor of the EEDF is introduced.…”

“…This means the curvature of the EEPF is positive and the probability function becomes a concave shaped bi‐Maxwellian distribution . This shape factor b is very sensitive to the microscopic variation of the process plasmas as described in the previous section, and the processed resultants, also . The coefficients c 1 and c 2 are determined by the normalization of the EEDF and the definition of the averaged effective electron temperature as: where Γ is the gamma function.…”

“…All EES parameters recorded during the HARC etching processes were classified into the gas mixing ratio and exhaustion group, RF power absorption condition group, spatial uniformity group, electro‐static chuck (ESC) condition group, and surface temperature group. These five groups of the EES parameters were parameterized into the PI parameters according to the methodology introduced in, on the basis of the accumulated mass production data of the 3 years. The value of D , described as the degree of defect particles, was replaced by the throughput loss in this HARC etching process.…”

Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

“…Sensor data refer to data acquired from the optical emission spectroscopy (OES) and voltage-current (VI) probe. It is known that the process results such as etch rate, deposition rate, and fault occurrence can be predicted using the data gathered during the process [ 4 , 5 , 6 , 7 , 8 ]. VM is basically a data process algorithm so it has the advantage of being able to check the process result in real-time during the process.…”

In situ monitoring of plasma ignition step in capacitively coupled plasma systems

“…The plasma emission spectrum includes the spectra of chemical species inside the chamber, and emitted-light spectra can be used to deduce electron temperature, where the amounts of radicals and ions are compared to find the endpoint of the etching process. [1][2][3][4][5][6] In developing a process for fine patterns, Mackus et al attempted to monitor improper plasma ignition in the ALD process and the failure of gas supply in the plasma-enhanced ALD process using OES. Nakane et al analyzed surface reactions during the ALE process using in situ Fourier transform infrared and spectroscopic ellipsometry.…”