| AVS 65th International Symposium & Exhibition | |
| Plasma Science and Technology Division | Thursday Sessions |
| Session PS-ThA |
| Session: | Plasma Diagnostics, Sensors and Controls |
| Presenter: | Sangwon Ryu, Seoul National University, Republic of Korea |
| Authors: | S. Ryu, Seoul National University, Republic of Korea H.-J. Roh, Seoul National University, Republic of Korea Y. Jang, Seoul National University, Republic of Korea D. Park, Seoul National University, Republic of Korea J. Koo, Seoul National University, Republic of Korea J.M. Lee, Seoul National University, Republic of Korea G.-H. Kim, Seoul National University, Republic of Korea |
| Correspondent: | Click to Email |
Advanced Process Control (APC) of plasma assisted processes has drawn interests because the reproducibility of process results is degraded by continuous deterioration of the equipment. To control the process drift, the process plasma should be maintained by in-situ controller. Some earlier researches handled real-time feedback proportional integral derivative (PID) controllers for plasma density which is coupled to generation of the reactive species in plasma assisted processes. However, since PID had no knowledge of the controlled system, PID couldn’t guarantee optimal control especially for systems with long dead time. Thus, we proposed model predictive controllers (MPC) for plasma density in Ar/SF6 etching plasma as the control model of the MPC contains information of the system. To provide plasma density to the controller in real-time, we developed plasma density monitoring module which used light emissions from Ar measured by a spectrometer. The method showed R2 = 0.99 with plasma density measured by Langmuir probe. The control model of the MPC was set as First Order Plus Dead Time (FOPDT) model which consisted of the linear gain and the time constants. We trained the control model with sensitivity tests; observing variation of plasma density as changing RF power. Compared to PID, MPC showed 6 times shorter settling time in set point tracking tests. Also, the integral of the absolute error for the MPC was 4 times lower than that of PID in same tests. The experimental results showed that MPC could control plasma more effectively than PID could by predicting the dead time of the system included in the control model. From the analysis on the parameters of the control model, we explained the control model as function of system parameters; the linear gain represented the balance between the power absorbed by electron and the power lost by electron impact collisions and the time constants were composed of the data transfer time between devices and the actuation time of the devices. This study showed that MPC could be used as the etching process plasma controller which would be a part of APC.