A Signature-based Approach for Diagnosis of Dynamic Faults in SRAMs

Ney, A.; Bosio, Alberto; Dilillo, Luigi; Girard, Patrick; Pravossoudovitch, S.; Virazel, Arnaud

doi:10.1109/dtis.2008.4540243

Cited by 3 publications

(5 citation statements)

References 24 publications

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“…It is the same way for the system. Signature-based approach for diagnosis is already used on RAM memory [2], the proposed methodology tries to apply this method to a complex system.…”

Section: Figure 2 Health Assessment Methodologymentioning

confidence: 99%

Normal signature characterization for system health assessment

Bect¹,

Simeu-Abazi

Maisonneuve³

et al. 2011

2011 Prognostics and System Health Managment Confernece

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Helicopter is a system on which operates a large number of specialities from electrical domain to mechanical domain. Today, diagnosis methods are segmented by field of expertise. Each expert treats the sub-system whose he is responsible for regardless of the results of other specialities.Therefore, there is no relation between specialities. Thus, diagnosis at system level is efficient but, due to the lack of correlation between subsystems, it is incomplete. Within the framework of a helicopter, the operator of maintenance uses all the data recorded during the flight, the results of expert treatments, but also his knowledge, his experience and his capacities of observation and analysis to provide an effective global diagnosis. In order to build relation between fields of expertise and so, to obtain a diagnosis at aircraft level which could be relevant, we try to set up a concept which gets closer, at most, to human judgment but which is not well adapted to industrial environment: the normality.During our study, we established that to reach our objective and to stick at best with the concept of normality, the most relevant solution consists in building a global normal signature. This signature could be illustrated as the image of the aircraft health, qualified as normal. This paper defines the normal signature and explains, in part, the process of its building.

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“…It is the same way for the system. Signature-based approach for diagnosis is already used on RAM memory [2], the proposed methodology tries to apply this method to a complex system.…”

Section: Figure 2 Health Assessment Methodologymentioning

confidence: 99%

Normal signature characterization for system health assessment

Bect¹,

Simeu-Abazi

Maisonneuve³

et al. 2011

2011 Prognostics and System Health Managment Confernece

View full text Add to dashboard Cite

show abstract

“…Secondly, most of the existing signature-based solutions target only the diagnosis of static faults. To the best of our knowledge, the diagnosis of dynamic faults has been considered in the literature only in [7,8]. In [7], the authors target both static and dynamic faults and propose new algorithms: March SD and the Aggressor Location Algorithm (ALA).…”

Section: Introductionmentioning

confidence: 99%

“…This approach can be considered as an "ad hoc" solution and can be classified among the techniques that introduce dedicated algorithms (and hence additional complexity) for diagnosis purpose. In [8], a signature-based approach for both static and dynamic faults is proposed and consists in the extension of the syndrome is operated without modifying the test algorithm. This extension is made by using information Paper 3.2 INTERNATIONAL TEST CONFERENCE 2 1-4244-4203-0/08/$20.00 ©2008 IEEE taken from the March test execution and from the memory structure itself.…”

Section: Introductionmentioning

confidence: 99%

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A History-Based Diagnosis Technique for Static and Dynamic Faults in SRAMs

Ney

Bosio

Dilillo

et al. 2008

2008 IEEE International Test Conference

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The usual techniques for memory diagnosis are mainly based on signature analysis. They consist in creating a fault dictionary that is used to determine the correspondence between the signature and the fault models affecting the memory. The effectiveness of such diagnosis methods is therefore strictly related to the fault dictionary accuracy. To the best of our knowledge, most of existing signature-based diagnosis approaches targets static faults only. In this paper, we present a new diagnosis approach that represents an alternative to signature-based approaches. This new diagnosis technique, named history-based diagnosis, makes use of the effect-cause paradigm already developed for logic design diagnosis. It consists in creating a database containing the history of operations (read and write) performed on a faulty memory core-cell. This information is crucial to track the root cause of the observed faulty behavior and it can be used to generate the set of possible Fault Primitives representing the set of suspected fault models. This new diagnosis method is able to identify static as well as dynamic faults. Although applied to SRAMs in this paper, it can be effective also for other memory types such as DRAMs. Experimental results are provided to prove the efficiency of the proposed methodology in generating a list of suspected faults as well as the location of the faulty components in the memory.

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“…Memory diagnosis approaches use various methods to identify and diagnose memory faults, e.g., fault pattern identification [6][7][8], fault signatures [1,9,10]. However, they hardly make any distinction between memory array faults and faults in other parts of the memory, such as decoders and peripherals.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Memory Diagnosis

Medeiros

Fieback

Gebregiorgis

et al. 2022

2022 IEEE European Test Symposium (ETS)

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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.

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A Signature-based Approach for Diagnosis of Dynamic Faults in SRAMs

Cited by 3 publications

References 24 publications

Normal signature characterization for system health assessment

Normal signature characterization for system health assessment

A History-Based Diagnosis Technique for Static and Dynamic Faults in SRAMs

Hierarchical Memory Diagnosis

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