Sangwon Ryu

Enhancement of the Virtual Metrology Performance for Plasma-Assisted Oxide Etching Processes by Using Plasma Information (PI) Parameters

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification or advanced process control models on to the real-mass production lines efficiently, high-performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this paper, as an effective method to include the “good information” representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b- and q-factor and surface passivation parameters as PCs into the PCR-based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient dataset provided cases. For mass production data of 350 wafers, developed PI-based VM model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

Recursive Model Estimation for the Plasma Parameters Quality Control

Abstract In recent years, plasma etching process has been considered important because it is one of the most critical processes in semiconductor manufacturing. Numerous research results have been presented in relation to the plasma etch processes, especially plasma parameters control. Although the above results are expected to bring about positive effects in various fields, they do not consider the variability of the system. In this paper, we performed the recursive model estimation of which the model structure is ARX considering the time delay. The recursive algorithm contains the Kalman filter interpretation to the model parameters. we have confirmed that the system model updated in real-time by our recursive model estimation algorithm continuously calculates the model parameters that predict the output variable precisely. Through these results, we expect the better performance of MPC control using the recursive model estimation

Improvement of dynamic range of electron energy probability function from two asymmetrical collecting area probe data filtered by Savitzky-Golay and Blackman window methods

The electron energy probability function (EEPF) measured by Langmuir probe is required to be reasonable in low energy regime and have large dynamic range (DR) in high energy regime to investigate the kinetics of low pressure plasma. However the internal resistance (Rint) in bias circuit of probe and the adaption of digital smoothing filter to increase DR destruct these requirements by distorting the EEPF in low energy regime. Rint is sum of the resistances due to the chamber wall sheath and surface of chamber wall. The existence of Rint gives distortion of measured EEPF in low energy regime by overestimating measured probe voltage. Adapting digital smoothing filter gives additional distortion of EEPF in low energy regime since it flattens the peak shape near zero electron energy. A new method is proposed to acquire EEPF which has reasonable value in low energy regime and large DR in high energy regime. The overestimated probe voltage is corrected by removing the effect of Rint which is determined from two sets of plasma potential (Vp) and electron saturation current (Ipe*). The Savitzky-Golay and Blackman window filters are adapted to the I-V characteristics of larger collecting area probe, which has larger signal-to-noise ratio. The two digital smoothing filters are optimized to maximize the strengths of each filter by considering the property of EEPF in low and high energy regime. The verification and capability evaluation of the proposed method are performed by comparing the EEPF measured from optical emission spectroscopy (OES) and conventional method based on single Langmuir probe. The method enhances DR of measured EEPF about 35 ~ 40 dB in comparison with the EEPF from conventional method, especially at two energy regions near zero electron energy and high energy. There are two requirements for proposed method. The distance between two probes is small enough to maintain that ΔVp due to the difference of measurement position is smaller than ΔVp due to Rint where ΔVp is the difference of Vp between two probes. Also signal-to-noise ratio of larger collecting area probe should be larger than 55 dB to ensure the performance of Savitzky-Golay method in low energy regime.

Monitoring Ion Energy Distribution in Capacitively Coupled Plasmas Using Non-invasive Radio-Frequency Voltage Measurements

A non-invasive method for ion energy distribution measurement at a RF biased surface is proposed for monitoring the property of ion bombardments in capacitively coupled plasma sources. To obtain the ion energy distribution, the measured electrode voltage is analyzed based on the circuit model which is developed with the linearized sheath capacitance on the assumption that the RF driven sheath behaves like a simple diode for a bias power whose frequency is much lower than the ion plasma frequency. The method is verified by comparing the ion energy distribution function obtained from the proposed model with the experimental result taken from the ion energy analyzer in a dual cathode capacitively coupled plasma source driven by a 100 MHz source power and a 400 kHz bias power.