Hierarchical Memory Diagnosis

G. C. Medeiros; M. Fieback; A. Gebregiorgis; M. Taouil; L. B. Poehls; S. Hamdioui

doi:10.1109/ETS54262.2022.9810467

Hierarchical Memory Diagnosis

G. C. Medeiros, M. Fieback, A. Gebregiorgis, M. Taouil, L. B. Poehls, S. Hamdioui

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

Original language	English
Title of host publication	Proceedings - 2022 IEEE European Test Symposium, ETS 2022
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	2
ISBN (Electronic)	9781665467063
DOIs	https://doi.org/10.1109/ETS54262.2022.9810467
Publication status	Published - 2022
Event	27th IEEE European Test Symposium, ETS 2022 - Barcelona, Spain Duration: 23 May 2022 → 27 May 2022

Publication series

Name	Proceedings of the European Test Workshop
Volume	2022-May
ISSN (Print)	1530-1877
ISSN (Electronic)	1558-1780

Conference

Conference	27th IEEE European Test Symposium, ETS 2022
Country/Territory	Spain
City	Barcelona
Period	23/05/22 → 27/05/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Algorithm
Diagnosis
Fault
Memory
Test

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10.1109/ETS54262.2022.9810467

Hierarchical_Memory_DiagnosisFinal published version, 1.22 MB

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title = "Hierarchical Memory Diagnosis",

abstract = "High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes. ",

keywords = "Algorithm, Diagnosis, Fault, Memory, Test",

author = "Medeiros, {G. C.} and M. Fieback and A. Gebregiorgis and M. Taouil and Poehls, {L. B.} and S. Hamdioui",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 27th IEEE European Test Symposium, ETS 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ETS54262.2022.9810467",

language = "English",

series = "Proceedings of the European Test Workshop",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "Proceedings - 2022 IEEE European Test Symposium, ETS 2022",

address = "United States",

}

Medeiros, GC, Fieback, M , Gebregiorgis, A , Taouil, M, Poehls, LB & Hamdioui, S 2022, Hierarchical Memory Diagnosis. in Proceedings - 2022 IEEE European Test Symposium, ETS 2022. Proceedings of the European Test Workshop, vol. 2022-May, Institute of Electrical and Electronics Engineers (IEEE), 27th IEEE European Test Symposium, ETS 2022, Barcelona, Spain, 23/05/22. https://doi.org/10.1109/ETS54262.2022.9810467

Hierarchical Memory Diagnosis. / Medeiros, G. C.; Fieback, M.; Gebregiorgis, A. et al.
Proceedings - 2022 IEEE European Test Symposium, ETS 2022. Institute of Electrical and Electronics Engineers (IEEE), 2022. (Proceedings of the European Test Workshop; Vol. 2022-May).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Hierarchical Memory Diagnosis

AU - Medeiros, G. C.

AU - Fieback, M.

AU - Gebregiorgis, A.

AU - Taouil, M.

AU - Poehls, L. B.

AU - Hamdioui, S.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

AB - High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

KW - Algorithm

KW - Diagnosis

KW - Fault

KW - Memory

KW - Test

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U2 - 10.1109/ETS54262.2022.9810467

DO - 10.1109/ETS54262.2022.9810467

M3 - Conference contribution

AN - SCOPUS:85134220078

T3 - Proceedings of the European Test Workshop

BT - Proceedings - 2022 IEEE European Test Symposium, ETS 2022

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 27th IEEE European Test Symposium, ETS 2022

Y2 - 23 May 2022 through 27 May 2022

ER -

Hierarchical Memory Diagnosis

Abstract

Publication series

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Bibliographical note

Keywords

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