A Comparison of Optimization Methods for the Transparent Conducting Oxide Application of Ga-doped ZnO

In this paper, statistical experimental design is used to characterize the transparent conducting oxide process of Ga-doped ZnO. Fractional factorial design with three center points are employed. In the process modeling, neural networks trained by the error back-propagation algorithm and genetic programming are applied to map the relationships between several input factors and resistivity. Both modeling methods are typical modeling methods for local and global approaches. Subsequently, both genetic algorithms and particle swarm optimization are used to identify the optimal process conditions to minimize resistivity. The results of the two approaches are compared, and the optimized resistivity found by the particle swarm method was slightly better than that found by genetic algorithms. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller standard deviations, implying greater consistency in recipe generation.