Improving Performance of Effect-Cause Diagnosis with Minimal Memory Overhead

Tang, Hong; Liu, Chen; Cheng, Wu-Tung; Reddy, S.M.; Zou, Wei

doi:10.1109/ats.2007.47

Cited by 9 publications

(23 citation statements)

References 26 publications

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“…In Figure 8, each stacked column represents the distribution of suspect count change for a defect type for a design. The change is segmented into five categories: fewer suspects (<0), no change (=0), between 1 and 5 ( [1,5]), between 6 and 10 ( [6,10]), and larger than 10 (> 10). For many of the designs and various fault types, more than 90% of the cases have a suspect count equal to or less than that of diagnosis using the original design.…”

Section: A Partitioning Results and Impact On Diagnosis Resultsmentioning

confidence: 99%

“…For design with millions of gates, the cause-effect methods require a large amount of storage. This problem can be relieved by compressing the fault dictionary [8] [9], nevertheless, the memory required for saving the compressed fault dictionary is still proportional to the size of the design and the number of test patterns rendering the inapplicable for large industrial designs with hundreds of millions of gates [10].…”

Section: Preliminariesmentioning

confidence: 99%

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Improved volume diagnosis throughput using dynamic design partitioning

Xiao

Tang

Huang

et al. 2012

2012 IEEE International Test Conference

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A method based on dynamic design partition is presented to increase the throughput of volume diagnosis by increasing the number of failing dies diagnosed within a given time T using given constrained computational resources C. Recently we proposed a static design partitioning method to reduce the diagnosis memory footprint for large designs [1] to achieve this objective. The method in [1] is applied once for each design without using the information of test patterns and failure files, and then diagnosis is performed on an appropriate block(s) of the design partition for a failure file. Even though the memory footprint of diagnosis is reduced the diagnosis quality is impacted to unacceptable levels for some types of defects such as bridges. In this paper, we propose a new failure dependent design partitioning method to improve volume diagnosis throughput with a minimal impact on diagnosis quality. For each failure file, the proposed method first determines the small partition needed to diagnose this failure, and then performs the diagnosis on this partition instead of the complete design. Since the partition is far smaller, both the run time and the memory usage of diagnosis can be significantly reduced better than when earlier proposed static partition is used. Extensive experiments were conducted on several large industrial designs to validate the proposed method. It has been observed that the typical partition size for various defects is less than 3% of the size of the original design. Also diagnosis runs much faster (>2X) on the partition. Combining these two factors, the throughput of volume diagnosis can be improved by an order of magnitude.

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Section: A Partitioning Results and Impact On Diagnosis Resultsmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Improved volume diagnosis throughput using dynamic design partitioning

Xiao

Tang

Huang

et al. 2012

2012 IEEE International Test Conference

Self Cite

View full text Add to dashboard Cite

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“…Effect-Cause Diagnosis [8], [9], [37] We know that cause-effect diagnosis paradigm is impractical for diagnosing large designs as the fault dictionary pre-built is too large to be accepted. However, one can build a fault dictionary of small size whose memory overhead is reasonable to speed-up the effect-cause diagnosis procedures [8], [9], [37]. The effect-cause algorithm used in the previous works [8], [9], [37] has two phases as shown in Figure 8.…”

Section: Fault Dictionaries Based Methods To Acceleratementioning

confidence: 99%

“…The size can be further reduced by recording only k failing test patterns at the cost of slightly degrading the resolution. Researchers in [8], [9] proposed a technique to compress the fault dictionary by using a multiple input signature register (MISR) to generate a compressed fault signature. One problem for this method is two difference test responses may be compressed to the same failing signature.…”

Section: Cause-effect Diagnosismentioning

confidence: 99%

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Fault diagnosis of VLSI designs

Xiao¹

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The modern VLSI circuit designs manufactured with advanced technology nodes of 65nm or below exhibit an increasing sensitivity to the variations of manufacturing process. New design-specific and feature-sensitive failure mechanisms are on the rise. Systematic yield issues can be severe due to the complex variability involved in process and layout features. Without improved yield analysis methods, time-to-market is delayed, mature yield is suboptimal, and product quality may suffer, thereby undermining the profitability of the semiconductor company. Diagnosis-driven yield improvement is a methodology that leverages production test results, diagnosis results, and statistical vi TABLE OF CONTENTS LIST OF TABLES .

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Defect Diagnosis Techniques for Silicon Customer Returns

Girard¹,

Bosio²,

Ladhar³

et al. 2023

Frontiers of Quality Electronic Design (QED)

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This chapter provides an overview of the various approaches and techniques proposed so far for defect diagnosis in silicon customer returns. It focuses on diagnosis of defects in logic blocks of SoCs. After some backgrounds on test and fault diagnosis, the chapter presents the various test scenarios used in practice during customer return diagnosis. A discussion on the quality required by the test sequences used during customer return is also proposed. Then, the chapter reviews the stateof-the-art techniques existing to identify defects at the cell level (called intra-cell or cell-aware diagnosis). A summary of conventional approaches is first proposed. Then, the latest Machine Learning (ML) -based cell-aware diagnosis techniques are reviewed. Effectiveness of existing ML techniques is shown through industrial case studies and corresponding diagnosis results in terms of accuracy and resolution. The chapter ends with a discussion on the future directions in this field.

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Improving Performance of Effect-Cause Diagnosis with Minimal Memory Overhead

Cited by 9 publications

References 26 publications

Improved volume diagnosis throughput using dynamic design partitioning

Improved volume diagnosis throughput using dynamic design partitioning

Fault diagnosis of VLSI designs

Defect Diagnosis Techniques for Silicon Customer Returns

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