Genetic-Algorithm-Based Model Parameter Extraction for Sub-100nm SOI MOSFET

LI Zun-chao; ZHANG Rui-zhi; ZHANG Xiao-juan; LIN Yao

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Genetic-Algorithm-Based Model Parameter Extraction for Sub-100nm SOI MOSFET

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- Genetic-Algorithm-Based Model Parameter Extraction for Sub-100nm SOI MOSFET
- Acta Electronica Sinica Vol. 35, Issue 11, Pages: 2033-2037(2007)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,陕西,西安,710049
  2. 青海师范大学计算机系,青海,西宁,810008
  3. 西安交通大学电子与信息工程学院陕西西安,710049
  4. 青海师范大学计算机系青海西宁,810008
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN432
- Published：2007
- 稿件说明：
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LI Zun-chao, ZHANG Rui-zhi, ZHANG Xiao-juan, et al. Genetic-Algorithm-Based Model Parameter Extraction for Sub-100nm SOI MOSFET[J]. Acta Electronica Sinica, 2007, 35(11): 2033-2037.
DOI：

LI Zun-chao, ZHANG Rui-zhi, ZHANG Xiao-juan, et al. Genetic-Algorithm-Based Model Parameter Extraction for Sub-100nm SOI MOSFET[J]. Acta Electronica Sinica, 2007, 35(11): 2033-2037. DOI：

摘要

为了简化亚100nm SOI MOSFET BSIMSOI4的模型参数提取过程

实现全局优化

使用了遗传算法技术

并提出了保留多个最优的自适应遗传算法.该算法通过保留最优个体的多个拷贝

对适应度高和适应度低的个体分别进行诱导变异和动态变异

在进化起始阶段和终止阶段分别执行随机交叉和诱导交叉

既具有全局优化特性

又加速了局部搜索过程

提高了最终解的质量.不同种群数和进化代数条件下的参数提取实例表明

该算法提取精度高、速度快

全局优化稳定性好;适当增加种群数

有利于加速算法的全局收敛过程.

Abstract

Genetic algorithm is used in BSIMSOI4 model parameter extraction for sub-100nm SOI MOSFETs to simplify extraction process and optimize parameters globally.An extraction algorithm called adaptive genetic algorithm maintaining multi-optimum is proposed.In the new algorithm

multiple copies of the optimum chromosome in each generation are kept

induced and dynamic mutations are carried out on chromosomes with larger and smaller fitness

respectively

and random and induced crossovers are executed in the early and late generations

respectively.The global optimization is maintained

the local searching is speeded up and the quality of the final solution is improved.Extraction examples under different population sizes and evolutionary steps show that the new algorithm has the advantages of higher accuracy

faster convergence

and reliable global optimization and that global convergence could be speeded up by increasing population sizes properly.

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