Hyperactive Faults Dictionary to Increase Diagnosis Throughput

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

doi:10.1109/ats.2008.16

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Some approaches use the fault simulation based dictionary for diagnosis existed before [1] [15] [16] [17], but the common challenges all come down to that it cannot be scaled to a larger partition as the dictionary size depends on the number of faults and patterns. Some researchers were able to apply dictionary approach on chain diagnosis since the number of faults are determined by the total number of scan cells which is usually ∼2 to 3 order of magnitudes smaller.…”

Section: A Db Generationmentioning

confidence: 99%

Online Scan Diagnosis - A Novel Approach to Volume Diagnosis

Huang

Gupta

Lingappan

et al. 2018

EasyChair Preprints

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Section: A Db Generationmentioning

confidence: 99%

Online Scan Diagnosis - A Novel Approach to Volume Diagnosis

Huang

Gupta

Lingappan

et al. 2018

EasyChair Preprints

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“…Many works have been published on improving the performance for diagnosis algorithm using various techniques, such as pattern sampling [7], fault dictionary [8] [9][10] [11], machine learning [12] and GPU-based simulation [13]. However, the high memory requirement for diagnosing very large designs is not addressed.…”

Section: Introductionmentioning

confidence: 99%

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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“…One issue is that it takes longer to diagnose a failing die for a larger design, because longer time is needed to simulate more gates. Works have been published on improving the performance for diagnosis algorithm using various techniques, such as pattern sampling [6], fault dictionary [7][8] [9], machine learning [10], GPU-based simulation [11]. Unfortunately the high memory requirement for diagnosing very large designs is not addressed.…”

Section: Introductionmentioning

confidence: 99%

On Using Design Partitioning to Reduce Diagnosis Memory Footprint

Xiao

Tang

Reddy

et al. 2011

2011 Asian Test Symposium

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Recently statistical yield learning based on volume diagnosis has become popular. Volume diagnosis requires a large amount of diagnosis results to be produced within a reasonable time. However, it is challenging to achieve the desired throughput for modern designs with continuously increasing size. In this paper, we propose a method to partition a design under diagnosis into blocks together with a diagnosis flow at the block level. The diagnosis throughput is improved because more diagnosis jobs can be run concurrently and each job runs faster due to the reduced memory. A measure is also proposed to estimate the impact on diagnosis caused by design partitioning. Experimental results on benchmark circuits and several industrial designs show that diagnosis using circuit blocks has minimal impact on diagnosis accuracy and resolution. It is also demonstrated that the proposed measure is a good metric in predicting the impact on diagnosis.

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Hyperactive Faults Dictionary to Increase Diagnosis Throughput

Cited by 9 publications

References 14 publications

Online Scan Diagnosis - A Novel Approach to Volume Diagnosis

Online Scan Diagnosis - A Novel Approach to Volume Diagnosis

Improved volume diagnosis throughput using dynamic design partitioning

On Using Design Partitioning to Reduce Diagnosis Memory Footprint

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