期刊文献+

基于故障注入的电路测试性仿真分析 被引量:2

Simulation Analysis of Circuit Testability Based on Fault Injection
下载PDF
导出
摘要 本文从集成电路基本测试理论和测试方法开始,逐步深入地对数模混合信号电路的测试性进行研究,从基本的复杂电路划分开始,对仿真的思维方法、流程以及一些关键步骤进行了分析。文章提出了将EDA仿真和故障注入相结合的电路测试性仿真方法,并证明该方法具有很强的工程实用价值。 This paper started from the basic testing theory and method of integrated circuit, and gradually studied the testability of logarithmic analog mixed-signal circuit in-depth, starting from the basic classification of complex cir- cuit, analyzed the thinking methods, processes, and some key steps of simulation. In this paper, a circuit testability simulation method combining the EDA Simulation with fault injection was proposed, and the method was proved with strong practical value in engineering.
作者 刘大川
出处 《河南科技》 2015年第4期12-15,共4页 Henan Science and Technology
关键词 测试性 故障仿真 虚拟仿真 仿真模型 testability fault simulation virtual simulation simulation model
  • 相关文献

参考文献4

二级参考文献32

  • 1龙兵,王日新,姜兴渭.多信号模型航天器配电系统最优测试技术[J].哈尔滨工业大学学报,2005,37(4):440-443. 被引量:11
  • 2龚勇,景小宁,陈云翔,吕振中,张林.基于多信号流图的飞控系统实时故障诊断[J].电光与控制,2006,13(6):89-92. 被引量:11
  • 3Deb S, Pattipati K R, Raghavan V, et al. Multi- signal flow graphs: a novel approach for system testability analysis and fault diagnosis[J]. IEEE Aerospace and Electronic Systems Magazine, 1995, 10(5): 14- 25.
  • 4Simpson W R, Sheppard J W. System testability assessment for integrated diagnostics[J]. IEEE Design & Test of Computers, 1992, 9(1): 40- 54.
  • 5Gould E. Modeling it both ways: hybrid diagnostic modeling and its application to hierarchical system designs[C]// Proceedings of IEEE Autotesteon. 2004 : 576 -582.
  • 6IEEE Standards Coordinating Committee 20. IEEE std 1522-2004. Trial-use standard for testability and diagnosability characteristics and metrics[S]. New York: Institue of Electrical and Electronics Engineers, 2004.
  • 7Bangso O, Wuillemin P H. Top-down construction and repetitive structures representation in Bayesian networks [C]//Proceedings of the 13th International Artificial Intelligence Research Society Conference. Florida, USA; AAAI Press, 2000: 282-286.
  • 8Skaanning C, Jensen F V, Kjzerulff U, et al. Acquisition and transformation of likelihoods to conditional probabilities for Bayesian networks[R]. AAAI SS-99-04, Stanford, USA, 1999.
  • 9Langseth H, Bangso O. Parameter learning in object-oriented Bayesian networks[J]. Annals of Mathematics and Artificial Intelligence, 2001(32): 221-243.
  • 10Hess A, Fila L. Prognostics, from the need to reality-- from the fleet users and PHM system designer/developers perspectives[C] //Proceedings of the IEEE Aerospace Conference. Big Sky: Montana, 2002(6) : 2791-2797.

共引文献34

同被引文献19

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部