Computational intelligence-based testing for noise and robustness analysis

Eric Liau; Manfred Menke; Thoma Janik; Doris Schmitt-Landsiedel

doi:10.1109/CIMSA.2005.1522878

Computational intelligence-based testing for noise and robustness analysis

Eric Liau
, Manfred Menke
, Thoma Janik
, Doris Schmitt-Landsiedel

Medicine and Health

Infineon Technologies AG

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Interconnect and power supply noise (PSN) is becoming more severe as technology scales, and can cause signal distortion and increase gate delay. This can further result in improper circuit operation. This paper describes a novel computational intelligence with ultra-short worst case test flow concept to detect a hang-up in a pseudo-SRAM test chip with asynchronous operation and hidden refresh, Package parasitics are found to be the cause of the failure, and debugging is performed by modification of the power network. This method allows finding worst case tests and identifying design weaknesses efficiently.

Original language	English
Title of host publication	Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Pages	279-284
Number of pages	6
DOIs	https://doi.org/10.1109/CIMSA.2005.1522878
State	Published - 2005
Event	2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 - Giardini, Naxos, Italy Duration: 20 Jul 2005 → 22 Jul 2005

Publication series

Name	Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Volume	2005

Conference

Conference	2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005
Country/Territory	Italy
City	Giardini, Naxos
Period	20/07/05 → 22/07/05

Access to Document

10.1109/CIMSA.2005.1522878

Cite this

Liau, E., Menke, M., Janik, T., & Schmitt-Landsiedel, D. (2005). Computational intelligence-based testing for noise and robustness analysis. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 (pp. 279-284). Article 1522878 (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005; Vol. 2005). https://doi.org/10.1109/CIMSA.2005.1522878

Liau, Eric ; Menke, Manfred ; Janik, Thoma et al. / Computational intelligence-based testing for noise and robustness analysis. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. pp. 279-284 (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005).

@inproceedings{db95fda98edf4d10875ab96490f173c5,

title = "Computational intelligence-based testing for noise and robustness analysis",

abstract = "Interconnect and power supply noise (PSN) is becoming more severe as technology scales, and can cause signal distortion and increase gate delay. This can further result in improper circuit operation. This paper describes a novel computational intelligence with ultra-short worst case test flow concept to detect a hang-up in a pseudo-SRAM test chip with asynchronous operation and hidden refresh, Package parasitics are found to be the cause of the failure, and debugging is performed by modification of the power network. This method allows finding worst case tests and identifying design weaknesses efficiently.",

author = "Eric Liau and Manfred Menke and Thoma Janik and Doris Schmitt-Landsiedel",

year = "2005",

doi = "10.1109/CIMSA.2005.1522878",

language = "English",

isbn = "0780390261",

series = "Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005",

pages = "279--284",

booktitle = "Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005",

note = "2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005 ; Conference date: 20-07-2005 Through 22-07-2005",

}

Liau, E, Menke, M, Janik, T & Schmitt-Landsiedel, D 2005, Computational intelligence-based testing for noise and robustness analysis. in Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005., 1522878, Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005, vol. 2005, pp. 279-284, 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005, Giardini, Naxos, Italy, 20/07/05. https://doi.org/10.1109/CIMSA.2005.1522878

Computational intelligence-based testing for noise and robustness analysis. / Liau, Eric; Menke, Manfred; Janik, Thoma et al.
Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. p. 279-284 1522878 (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Janik, Thoma

AU - Schmitt-Landsiedel, Doris

PY - 2005

Y1 - 2005

N2 - Interconnect and power supply noise (PSN) is becoming more severe as technology scales, and can cause signal distortion and increase gate delay. This can further result in improper circuit operation. This paper describes a novel computational intelligence with ultra-short worst case test flow concept to detect a hang-up in a pseudo-SRAM test chip with asynchronous operation and hidden refresh, Package parasitics are found to be the cause of the failure, and debugging is performed by modification of the power network. This method allows finding worst case tests and identifying design weaknesses efficiently.

AB - Interconnect and power supply noise (PSN) is becoming more severe as technology scales, and can cause signal distortion and increase gate delay. This can further result in improper circuit operation. This paper describes a novel computational intelligence with ultra-short worst case test flow concept to detect a hang-up in a pseudo-SRAM test chip with asynchronous operation and hidden refresh, Package parasitics are found to be the cause of the failure, and debugging is performed by modification of the power network. This method allows finding worst case tests and identifying design weaknesses efficiently.

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DO - 10.1109/CIMSA.2005.1522878

M3 - Conference contribution

AN - SCOPUS:33749077987

SN - 0780390261

SN - 9780780390263

T3 - Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005

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BT - Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005

T2 - 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005

Y2 - 20 July 2005 through 22 July 2005

ER -

Liau E, Menke M, Janik T, Schmitt-Landsiedel D. Computational intelligence-based testing for noise and robustness analysis. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005. 2005. p. 279-284. 1522878. (Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2005). doi: 10.1109/CIMSA.2005.1522878

Computational intelligence-based testing for noise and robustness analysis

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