Optimizing semiconductor devices by self-organizing particle swarm
Neural and Evolutionary Computing
2007-05-23 v1
Abstract
A self-organizing particle swarm is presented. It works in dissipative state by employing the small inertia weight, according to experimental analysis on a simplified model, which with fast convergence. Then by recognizing and replacing inactive particles according to the process deviation information of device parameters, the fluctuation is introduced so as to driving the irreversible evolution process with better fitness. The testing on benchmark functions and an application example for device optimization with designed fitness function indicates it improves the performance effectively.
Cite
@article{arxiv.cs/0505067,
title = {Optimizing semiconductor devices by self-organizing particle swarm},
author = {Xiao-Feng Xie and Wen-Jun Zhang and De-Chun Bi},
journal= {arXiv preprint arXiv:cs/0505067},
year = {2007}
}
Comments
Congress on Evolutionary Computation, 2004. CEC2004. Volume: 2, On page(s): 2017- 2022 Vol.2