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A low pressure etching of silicon carbide is qualitatively characterized by using a neural network. To construct a predictive model, the etch process was characterized by means of a 25 full factorial experiment. Experimental factors that were varied include radio frequency (rf) source power, bias power, pressure, O2 fraction, and gap between the plasma source and wafer. An additional 15 experiments were conducted to test the appropriateness of the trained model. An optimized etch rate model has a root mean-squared error of 12.78 nm/min. Model response surface behaviors were certified by actual measurements. Several noticeable features at lower pressure etching include a lower etch rate, inverse relationship between the source power level and the dc bias, and a smaller etch rate variation with the source power. The effect of the bias power on the etch rate or dc bias was affected little by the pressure level. Etch mechanisms for the gap variations were quite different depending on the bias powers. Several etching aspects useful for plasma control were revealed.

Refractive index of silicon nitride film deposited in a plasma enhanced chemical deposition system is modeled by using neural network. The deposition process was characterized with a full factorial experiment. Additional 12 experiments were conducted to test model appropriateness. Predicted model behaviors were in good agreement with actual measurements. Deposition mechanisms were qualitatively examined especially with respect to the pressure. Possible interactions between the pressure and other factors (SiH4, NH3, radio frequency (RF) power, and substrate temperature) were examined on the basis of SiH/NH bond ratio. The refractive index increased with increasing either SiH4 flow rate or RF power. In contrast, the refractive index decreased with increasing NH3 flow rate. Little interactions between the pressure and RF power were observed. Pressure effect on the refractive index was quite different depending on the level of SiH4 flow rate or substrate temperature. In general, increasing the pressure increased the refractive index. Meanwhile, the refractive index was insensitive to parameter variations at relatively high pressures. The most complex interaction occurred as the pressure interacted with the temperature. Useful clues to control the refractive index were revealed from the predictive model.

Uniformity of plasma etching has been conventionally examined only in terms of etch rate. A uniformity of etching profile surface is increasingly demanded to improve process quality. This is accomplished by applying a discrete wavelet transformation (DWT) to profile images obtained with a scanning electron microscopy. Applicability of DWT-based profile uniformity was evaluated with a tungsten etch experiment, conducted in an SF/sub 6/ helicon plasma. Its suitability was investigated as a function of process parameters and scale levels. Compared to a conventional metric, the wavelet-based one characterized more effectively the uniformity of profile variations. The proposed metric can be applied to any other plasma-processed surfaces.

By controlling the proximity relative to plasma source, silicon carbide (SiC) has been etched in a C2F6 inductively coupled plasma. This was accomplished by adjusting the gap between the wafer substrate and the plasma source. Gap effects on SiC etching were extensively examined as a function of process factors as well as at particular two conditions, high source power (plasma density) and high bias power (ion bombardment). Decreasing the gap increased the etch rates for all conditions mainly due to the increased plasma density. The DC bias induced with the gap variation played a little role in affecting the etch rate. Gap effect was more enhanced as the wafer electrode was powered with relatively larger DC bias. Factor effects on the etch rates were quite different depending on the level of plasma density or ion bombardment. Decreasing the gap resulted in a microtrench at the base of the sidewall, which was little affected by the DC bias.

Summary form only given, as follows. Deposition of silicon nitride (SiN) film is one of the most critical processes that determine the efficiency of solar cells. Qualities of SiN film deposited by a plasma-enhanced chemical vapor deposition depend on many process parameters. Predicting film properties is very important to their optimization as well as to gain insight into underlying deposition mechanisms. For plasma-driven processes, however, it has been a difficult task to construct prediction models due to complexity within a plasma. In this study, a predictive model for a SiN PECVD process was constructed and used to understand physical deposition mechanisms. The interpretation was mainly focused on the refractive index, particularly with respect to the substrate temperature.

Qualitative models of plasma etching are essential for understanding physical etch behaviors as well as plasma control. Artificial neural networks (ANN) have been widely used in constructing predictive etch models. In most applications, the ANN prediction performance has been examined only in terms of the training factors. A technique for building a predictive model is presented here. This is accomplished by applying a neural network to classification data while optimizing the effect of the data boundary. The technique was evaluated with plasma etch data, characterized with a statistical experimental design. The etch response modeled included the etch rates for silica and aluminum (Al) as well as Al selectivity. Compared to the models for nonclassified data, the classification-based model demonstrated improvement in the prediction errors of 36.3 and 15.8% for the Al and silica etch rates, respectively, while exhibiting a poorer prediction for Al selectivity. However, by controlling the data boundary the Al selectivity model was improved significantly by about 43.8%. Thus, the classification-based modeling technique in conjunction with the control of the data boundary is effective in improving the prediction ability of neural network models.

A prediction model for surface roughness was constructed using a neural network and atomic force microscopy. The silicon carbide etch process was characterized by a 25 full factorial experiment. The experimental ranges of process parameters were 600–900W source power, 50–150W bias power, 4–16mTorr pressure, 0–80% O2 percentage, and 6–12cm gap. The model factors were optimized by means of a genetic algorithm. The optimized model had a root mean-squared error of 0.11nm. From the model, various plots were predicted while being supported by actual measurements. The dc bias induced by each process parameter was correlated to the surface roughness. Increasing the bias power increased the surface roughness. In contrast, the surface roughness decreased as the dc bias was larger than about 600V. The surface roughness was strongly correlated to the source power-induced dc bias only at low bias powers. The pressure effect was clear only as the dc bias was maintained at 480V. For the variations in the O2 percentage, ...