High Level Modeling of Signal Integrity in Field Bus Communication with SystemC-AMS
被引:0
|
作者:
Wang, Ruomin
论文数: 0引用数: 0
h-index: 0
机构:
UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, FranceUPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
Wang, Ruomin
[1
]
Denoulet, Julien
论文数: 0引用数: 0
h-index: 0
机构:
UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, FranceUPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
Denoulet, Julien
[1
]
Feruglio, Sylvain
论文数: 0引用数: 0
h-index: 0
机构:
UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, FranceUPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
Feruglio, Sylvain
[1
]
Vallette, Farouk
论文数: 0引用数: 0
h-index: 0
机构:
UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, FranceUPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
Vallette, Farouk
[1
]
Garda, Patrick
论文数: 0引用数: 0
h-index: 0
机构:
UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, FranceUPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
Garda, Patrick
[1
]
机构:
[1] UPMC Univ Paris 06, Lab Informat Paris 6, UMR 7606, F-75005 Paris, France
来源:
2012 19TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS)
|
2012年
关键词:
High-level modeling;
signal integrity;
field bus;
SystemC-AMS;
D O I:
暂无
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
This paper presents a novel method for modeling the functionality of a mixed-signal system, and analyzing its signal integrity (SI) at a high-level of abstraction with SystemC-AMS. Our model includes on a unique platform a functional module and a non-functional module. The functional module represents the operative behavior of the system and the nonfunctional module, based on neural network techniques, displays the SI characteristics of the system. The proposed method is demonstrated by modeling field bus communication system with two nodes. We achieved an error of about 3% for the neural network based Time Data Flow (TDF) model with respect to a RLC Electrical Linear Networks (ELN) model.