Optimization of transmittance characteristic of indium tin oxide film using neural networks

Indium thin oxide (ITO) films were deposited by a DC-powered plasma sputtering system. The transmittance characteristics of ITO film are investigated and optimized by using a neural network model and by a statistical factor analysis. For systematic modeling, 25-1 with a resolution V design was utilized for 5 process parameters including wafer temperature, DC power, chamber pressure, Cesium (Cs) canister temperature, and Cs carrier flow rate. A generalized regression neural network was used to build a model of transmittance. The statistical parameter analysis revealed a larger main effect of the power and pressure over the others. The prediction performance of genetic algorithm-optimized model is 2.70 in the root mean square error. The impact of the carrier flow rate or the wafer temperature was insensitive to the variation in the power. An increase in the transmittance was noted as either the Cs temperature or the pressure increased in particular at a lower power. The impact of the wafer temperature and carrier flow rate was the opposite of those for the Cs temperature and pressure. A high transmittance at a low surface roughness was noted as a function of the power and the Cs carrier flow rate.