Multi-trench-based technique to improve amorphous SiGe thin-film solar cell performance
In order to improve the electrical performance of the conventional amorphous SiGe thin-film solar cells, a new multi-trench technique has been proposed. In this method, the multi-trench is created in the intrinsic SiGe layer and filled with p-type doped Si. The p-type trenches in the intrinsic SiGe layer improve the electrical performance of the proposed design. Electrical characteristics of the proposed structure are analyzed and optimized by 2-D numerical modeling and compared with conventional amorphous SiGe thin-film solar cell characteristics. The extracted results show that the multi-trench-based technique has excellent effect on the fill factor and efficiency of the device.
Numerical Simulations of Amorphous and Polycrystalline Silicon Thin-Film Transistors
In this paper we present results of two-dimensional numerical simulations of both amorphous silicon and NMOS and PMOS polycrystalline silicon thin-film transistors. Both types of devices are modeled using an effective medium approach whereby the defects and grain boundaries in the material are treated as a spatially uniform density of localized states in the band gap. The field-effect mobility is self-consistently calculated from the appropriate band mobility and using one set of parameters for each material we obtain very good agreement between simulations of both output and transfer characteristics and experimental data. The experimental activation energy of the source-drain current for the polycrystalline devices is also found to be in excellent agreement with the numerical simulations.
Adaptive multi-objective genetic algorithms for scheduling of drilling operation in printed circuit board industry
In this paper, a scheduling problem for drilling operation in a real-world printed circuit board factory is considered. Two derivatives of multi-objective genetic algorithms are proposed under two objectives, i.e. makespan and total tardiness time. The proposed algorithms possess a rare characteristic from traditional multi-objective genetic algorithms. The crossover and mutation rates of the proposed algorithms can be variables or adjusted according to the searching performance while the rates of traditional algorithm are fixed. Production data retrieved from the shop floor are used as the test instances. The numerical result indicates that both two proposed multi-objective genetic algorithms have satisfactory performance and the adaptive multi-objective genetic algorithm performs better. The result shows the algorithms are effective and efficiency to the current system used in the shop floor. Thus, the result may be of interest to practical applications.