首页> 外文会议>International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design >Parameter Extraction Method Using Hybrid Artificial Bee Colony Algorithm for an OFET Compact Model
【24h】

Parameter Extraction Method Using Hybrid Artificial Bee Colony Algorithm for an OFET Compact Model

机译:OFET紧凑模型的混合人工蜂群算法参数提取方法

获取原文

摘要

Research on organic field effect transistors (OFETs) have been dramatically increased in the last decade, considering their lightweight and flexible structure as well as their practical and low-cost production. Building compact models and parameter extraction methods have also a critical importance in extensively using OFETs in electronic systems. In this paper, we propose a hybrid artificial bee colony algorithm as a parameter extraction tool and we compare it with purely mathematical and genetic algorithm-based parameter extraction methods. We apply these methods to a well-known OFET compact model for two different transistors, both having pentacene as organic semiconductor. First transistor (Tl) is available in literature and the other one (T2) is fabricated in our laboratory. The proposed approach shows a good agreement with the experimental data of Tl with normalized RMS error (NRMSE) of 0.26%. However, it is 1.83% for T2 due to lack of measurements. If the shape of the data was same, the parameter extraction approaches would be expected to perform more successfully for both OFETs as well. Results are tabulated and performances of the methods are compared in the paper.
机译:考虑到有机场效应晶体管(OFET)的轻巧和灵活的结构以及其实用和低成本的生产,在过去十年中,其研究已大大增加。建立紧凑的模型和参数提取方法对于在电子系统中广泛使用OFET也具有至关重要的意义。在本文中,我们提出了一种混合人工蜂群算法作为参数提取工具,并将其与基于纯数学和遗传算法的参数提取方法进行了比较。我们将这些方法应用于两个不同的晶体管的众所周知的OFET紧凑模型,这两个晶体管均具有并五苯作为有机半导体。第一个晶体管(T1)在文献中可用,而另一个(T2)在我们的实验室中制造。所提出的方法与T1的实验数据具有良好的一致性,归一化RMS误差(NRMSE)为0.26%。但是,由于缺乏测量,T2为1.83%。如果数据的形状相同,则对于两个OFET,参数提取方法也有望更成功地执行。结果列于表中,并对方法的性能进行了比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号