A Novel Nanogap Fabrication Technique Using Taguchi Method and Modified Particle Swarm Optimization

2012 ◽  
Vol 236-237 ◽  
pp. 118-122
Author(s):  
Te Sheng Li ◽  
Ling Hui Chen

In this study, a novel nanogap fabrication technique is proposed. The technique is based on electron-beam lithography combined with rapid thermal annealing (RTA) to reduce the self-aligned nanogap on metal layer. The procedure running through systematic experimental design via Taguchi method and considering the critical factors such as metal type, Si thickness, RTA temperature, RTA time and initial nanogap dimension affecting the final nanogap dimensions was optimized. The experiments were conducted using Taguchi method and modified particle swarm optimization for setting the optimal parameters. The experimental results show that the most important factors in nanogap reduction were the metal type and the initial nanogap. The optimal parameter settings were metal type Pt on 50 nm Si/SiO2, 400°C, 60s and 43nm for initial gap. Experiment results found that the metal type Pt provided larger shrink ratio than that of Ni and nanogap down to 30 nm. It is also noted that the proposed approach was reproducible due to the confirmation experiments SNRs within the 95% confidence interval.

Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.


Sign in / Sign up

Export Citation Format

Share Document