Computational intelligence technique in optimization of nano-process deposition parameters

Norlina, M. S.; Mazidah, P.; Sin, N. D. Md; Rusop, M.

doi:10.1109/ceec.2015.7332722

Cited by 1 publication

(1 citation statement)

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“…This trial and error process could take weeks to complete, including the time taken for the thin film measurements process afterwards. In order to reduce the time taken and costs in the fabrication process, a computational experiment could be performed to optimize the process parameters before any actual laboratory works are done [4,5]. The computational experiment could complement the conventional method of process parameters optimization and is expected to reduce the number of laboratory experiments that have to be conducted in the fabrication process.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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This research is focusing on the radio frequency (RF) magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA) technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Immune System (AIS) and Ant Colony Optimization (ACO). Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

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Section: Introductionmentioning

confidence: 99%