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Modeling a Via Profile Etched in a CHF3/CF4 Plasma Using a Neural Network
ByungwhanKim,권광호,Sung-KuKwon,Jong-MoonPark,Seong-WookYoo,Kun-SikPark,In-KyuYou,Bo-WooKim 한국물리학회 2002 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.41 No.4
Via profiles of oxide films were qualitatively modeled using a neural network. The oxide films were magnetically etched in a CHF$_3$/CF$_4$ plasma with various radio-frequency (RF) powers, pressures, and CHF$_3$ and CF$_4$ flow rates. A statistical 2$^{4-1}$ fractional factorial experiment was conducted to characterize the behavior of the via profile. The neural network was trained on nine experiments, and the trained model was evaluated on another eight experiments, not belonging to the training data. As a function of the training factors, the prediction accuracy of profile model was optimized, and the optimized model had a prediction error of 3.05$^\circ$. Compared to the statistical regression model, this was about a 43 \% improvement in the prediction accuracy. Using the model, we made several 3-D plots to unveil underlying etch mechanisms, including the factor interaction effects, involved in via formation. As expected, the profile angle decreased with increasing RF power without regard to the pressure. The DC bias induced by the pressure played an important role in affecting the profile angle. The profile became more positively sloped with increasing the CHF$_3$ flow rate, contrary to what was noticed with the variation in the CF$_4$ flow rate. For the profiles to be positively sloped, the effects of either pressure or CHF$_3$ flow rate must be more noticeable than they are for the profiles to be negatively sloped.
Modeling of Sidewall Bottom Etching Using a Neural Network
Byungwhan Kim,Dongil Han,Kyeong Kyun Lee,Seungbin Moon 한국물리학회 2005 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.46 No.2
The discrepancy in the sidewall bottom etch rate with respect to the center etch rate, referred to as DSE, should be minimized to achieve a uniform surface etching. A neural network model was constructed to investigate the etching characteristics of DSE. The experimental data were collected during a via formation by using a magnetically enhanced reactive ion etch system. To systematically characterize the etch process, we employed a statistical experimental design. To evaluate the contributions from polymer deposition, chemical etching, or physical ion bombardment, we collected important radicals (such as CF or F) by using optical emission spectroscopy and the dc bias. For decreasing CF4 flow rates, the smaller DSE was attributed to enhanced polymer deposition. Both polymer deposition and ion bombardment were determined to be the main contributors to the smaller center etch rate at higher power. The model suggest that a large dc bias should be induced to achieve a smaller DSE.
Effect of Room-Temperature Ion Energy on PECVD-SiN Films
Byungwhan Kim,Suyeon Kim 한국물리학회 2009 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.54 No.3
Silicon-nitride (SiN) films were deposited at room temperature by using a plasma-enhanced chemical vapor deposition system. Ion bombardment actively involved in the SiN growth mech- anism was detailed by measuring the ion energy and the ion energy flux with a non-invasive ion energy analyzer. Diagnostic parameters examined included a low ion energy, a high ion energy, a low ion energy flux and a high ion energy flux. From these, two diagnostic parameters, the ion energy difference (IED) and the ion flux ratio (IFR), were calculated. The IED and IFR strongly depended on the high ion energy and the high ion energy flux, respectively. Both the high ion energy and the IED were closely related to the SiN deposition rate. An empirical relationship between the deposition rate and the diagnostic parameters was derived. Silicon-nitride (SiN) films were deposited at room temperature by using a plasma-enhanced chemical vapor deposition system. Ion bombardment actively involved in the SiN growth mech- anism was detailed by measuring the ion energy and the ion energy flux with a non-invasive ion energy analyzer. Diagnostic parameters examined included a low ion energy, a high ion energy, a low ion energy flux and a high ion energy flux. From these, two diagnostic parameters, the ion energy difference (IED) and the ion flux ratio (IFR), were calculated. The IED and IFR strongly depended on the high ion energy and the high ion energy flux, respectively. Both the high ion energy and the IED were closely related to the SiN deposition rate. An empirical relationship between the deposition rate and the diagnostic parameters was derived.
Modeling of a hemispherical inductively coupled plasma using neural network
Byungwhan Kim,Suyeon Kim 한국물리학회 2009 Current Applied Physics Vol.9 No.1
In this study, a hemispherical inductively coupled plasma (HICP) was modeled by using a neural network called a radial basis function network (RBFN). The prediction performance of RBFN models were optimized by using a genetic algorithm. Using a Langmuir probe, experimental data were collected from the HICP equipment of 10 turns. For a systematic modeling, plasma discharge was characterized by using a statistical experiment. The process parameters involved include a radio frequency source power, pressure, position of probe tip, and Cl2 flow rate. The plasma characteristics modeled include plasma density and electron temperature. From the optimized models, 3D plots were generated to explore parameter effects. Plasma density (or electron temperature) was the most strongly dependent on the tip position. The effect of source power on plasma density was almost independent of Cl2 flow rate. The effect of pressure was inclined to slightly decrease plasma density. Unlike in other plasma sources, electron temperature was little affected by pressure. The effect of Cl2 flow rate of increasing electron temperature was the most significant under higher plasma density. In this study, a hemispherical inductively coupled plasma (HICP) was modeled by using a neural network called a radial basis function network (RBFN). The prediction performance of RBFN models were optimized by using a genetic algorithm. Using a Langmuir probe, experimental data were collected from the HICP equipment of 10 turns. For a systematic modeling, plasma discharge was characterized by using a statistical experiment. The process parameters involved include a radio frequency source power, pressure, position of probe tip, and Cl2 flow rate. The plasma characteristics modeled include plasma density and electron temperature. From the optimized models, 3D plots were generated to explore parameter effects. Plasma density (or electron temperature) was the most strongly dependent on the tip position. The effect of source power on plasma density was almost independent of Cl2 flow rate. The effect of pressure was inclined to slightly decrease plasma density. Unlike in other plasma sources, electron temperature was little affected by pressure. The effect of Cl2 flow rate of increasing electron temperature was the most significant under higher plasma density.
Magnetic Field Effect on Al Etching in a Chlorine Plasma Discharge
Byungwhan Kim,Dongil Han,Duk Woo Lee 한국물리학회 2005 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.46 No.2
The eect of a magnetic eld on aluminum lms is examined under various plasma conditions. For this, the etch process was characterized by a 26-1 fractional factorial experiment. The experimental ranges of six process parameters include 50 250 W for the radio frequency power, 10 100 Torr for the pressure, 10 100 G for the magnetic eld strength, 10 70 sccm for the Cl2 ow rate,20 80 sccm for the BCl3 ow rate, and 20 80 sccm for the N2 ow rate. Relationships between the process parameters and the etch rate were modeled using a backpropagation neural network. To test the appropriateness of the trained model, we conducted 12 additional experiments. The increase in the etch rate with increasing magnetic eld strength was attributed to an enhanced plasma density. The dc bias facilitated the interpretation of parameter eects. In particular, the increased etch rate at lower pressure was ascribed to the enhanced ion bombardment that occurred at a large magnetic feld strength.