Optimization of Cell-Aware ATPG Results by Manipulating Library Cells' Defect Detection Matrices

Gao, Zhan; Hu, Min‐Chun; Swenton, Joe; Malagi, Santosh; Huisken, Jos; Goossens, Kees; Marinissen, Erik Jan

doi:10.1109/itc-asia.2019.00029

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…They are called intra-cell or cell-internal defects [1][2][3]. These defects are only covered fortuitously with conventional fault models, and hence not surprisingly, these defects are found to be the root cause of a significant fraction of test escape [4].…”

Section: Introductionmentioning

confidence: 99%

Cell-Aware Model Generation Using Machine Learning

d'Hondt¹,

Ladhar²,

Girard³

et al. 2023

Frontiers of Quality Electronic Design (QED)

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Characterizing cell-internal defects of standard cell libraries is an essential step to ensure high test and diagnosis quality. However, such a characterization process, called cell-aware model generation, usually resorts to extensive electrical defect simulations that are costly in terms of run time and utilization of SPICE simulator licenses. Typically, the generation time of cell-aware models for few hundreds of cells may reach up to several months considering a single SPICE license. This chapter presents a methodology that does not use any electrical defect simulation to predict the response of a cell-internal defect once it is injected in a standard cell. More widely, this methodology uses existing cell-aware models (generated from electrical simulations) from various standard cell libraries and technologies to predict cellaware models (learning-based) for new standard cells independently of the technology. Experiments done on several industrial cell libraries using different technologies demonstrate the accuracy and performance of the prediction method.

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Section: Introductionmentioning

confidence: 99%

Cell-Aware Model Generation Using Machine Learning

d'Hondt¹,

Ladhar²,

Girard³

et al. 2023

Frontiers of Quality Electronic Design (QED)

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“…Cell-Aware (CA) test and diagnosis techniques have been introduced to target defects located inside the std cells (referred to as intra-cell or cell-aware defects) which are only fortuitously covered by traditional fault models and therefore are often found to be the root cause of a significant fraction of test escape with modern technology nodes [2][3][4][5]. CA techniques rely on the realistic assumption that the excitation of a defect inside a std cell is correlated with the logic values applied to its inputs [5][6].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Learning-Based Flow for Cell-Aware Model Generation

d'Hondt

Ladhar

Girard

et al. 2022

2022 IEEE International Test Conference (ITC)

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As the semiconductor industry continues to shrink the transistor feature size, new fault models need to be invented and deployed to ensure manufacturing test and diagnostic of the highest quality. The Cell-Aware (CA) test and diagnosis methodology targets the detection of defects inside standard (std) cells, at the transistor level. While becoming an industry standard, the CA methodology, has a large and costly deployment overhead, involving numerous analog simulations. In [1], we presented an innovative flow using Machine-Learning (ML) to reduce the CA test method runtime and ease its adoption for industrial usage. Experiments using different technology nodes demonstrated an over 99% runtime reduction for 80% of combinational cells. In this paper, new elements are presented to more widely take advantage of the ML flow for CA characterization. This includes a new decision algorithm, leveraging ML techniques to decide whether the CA characterization of a new std cell should be MLbased or simulation-based, thus allowing to decrease the CA characterization runtime while maintaining high quality CA models for all cells. Experimental results demonstrate the high performance of the new decision algorithm. The fault coverage on real cell-internal defects of ATPG patterns using ML predicted CA data proves that our predicted CA data can accurately replace those obtained by running extensive analog simulations, thus proving the effectiveness and pertinence of the proposed methodology.

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“…Several articles focusing on cell-aware testing can be found in literature [13]- [16]. These works focus on either proposing new algorithms to optimize cell-aware ATPG performances as in [13], [15], exploiting switch-level ATPGs to generate test patterns for CAT together with algorithms to automatically inject defects into the network to be tested [16], or focusing on intra-cell defects to improve cell-aware test methodologies, such as intra-cell bridging faults [14]. In-field testing of such defects through SBST, however, has never been investigated.…”

Section: Introductionmentioning

confidence: 99%

Recent Trends and Perspectives on Defect-Oriented Testing

Bernardi

Cantoro

Coyette

et al. 2022

2022 IEEE 28th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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Electronics employed in modern safety-critical systems require severe qualification during the manufacturing process and in the field, to prevent fault effects from manifesting themselves as critical failures during mission operations. Traditional fault models are not sufficient anymore to guarantee the required quality levels for chips utilized in mission-critical applications. The research community and industry have been investigating new test approaches such as device-aware test, cellaware test, path-delay test, and even test methodologies based on the analysis of manufacturing data to move the scope from 0PPM to 0PPB. This special session presents four contributions, from academic researchers and industry professionals, to enable better chip quality. We present results on various activities towards this objective, including device-aware test, software-based self-test, and memory test.

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Optimization of Cell-Aware ATPG Results by Manipulating Library Cells' Defect Detection Matrices

Cited by 10 publications

References 14 publications

Cell-Aware Model Generation Using Machine Learning

Cell-Aware Model Generation Using Machine Learning

A Comprehensive Learning-Based Flow for Cell-Aware Model Generation

Recent Trends and Perspectives on Defect-Oriented Testing

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