Defect-Location Identification for Cell-Aware Test

Gao, Zhan; Malagi, Santosh; Marinissen, Erik Jan; Swenton, Joe; Huisken, Jos; Goossens, Kees

doi:10.1109/latw.2019.8704561

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…The experiment results show that for 351 combinational cells in Cadence's GPDK045 45nm technology, we reduce 96.4% simulation time comparing with simulating the full set of defects. This paper extends our work in [11] with a refined PEX cell model, an improved solution for reducing the defect simulation time with maintaining the test quality, and additional experiment results on defect simulation.…”

Section: Introductionmentioning

confidence: 71%

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Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Gao

Malagi

et al. 2021

J Electron Test

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Cell-aware test (CAT) explicitly targets faults caused by defects inside library cells to improve test quality, compared with conventional automatic test pattern generation (ATPG) approaches, which target faults only at the boundaries of library cells. The CAT methodology consists of two stages. Stage 1, based on dedicated analog simulation, library characterization per cell identifies which cell-level test pattern detects which cell-internal defect; this detection information is encoded in a defect detection matrix (DDM). In Stage 2, with the DDMs as inputs, cell-aware ATPG generates chip-level test patterns per circuit design that is build up of interconnected instances of library cells. This paper focuses on Stage 1, library characterization, as both test quality and cost are determined by the set of cell-internal defects identified and simulated in the CAT tool flow. With the aim to achieve the best test quality, we first propose an approach to identify a comprehensive set, referred to as full set, of potential open- and short-defect locations based on cell layout. However, the full set of defects can be large even for a single cell, making the time cost of the defect simulation in Stage 1 unaffordable. Subsequently, to reduce the simulation time, we collapse the full set to a compact set of defects which serves as input of the defect simulation. The full set is stored for the diagnosis and failure analysis. With inspecting the simulation results, we propose a method to verify the test quality based on the compact set of defects and, if necessary, to compensate the test quality to the same level as that based on the full set of defects. For 351 combinational library cells in Cadence’s GPDK045 45nm library, we simulate only 5.4% defects from the full set to achieve the same test quality based on the full set of defects. In total, the simulation time, via linear extrapolation per cell, would be reduced by 96.4% compared with the time based on the full set of defects.

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Section: Introductionmentioning

confidence: 71%

“…To save downstream defect characterization time, In Step 3, we collapse the full set to a compact set based on the defect equivalence [11]. As equivalent defects are detected by the same set of cell patterns which is identified by analog simulation, per group of equivalent defects simulating only one defect is enough.…”

Section: Library Characterization Flowmentioning

confidence: 99%

Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Gao

Malagi

et al. 2021

J Electron Test

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“…In this later case, only the second vector of a dynamic test pattern is considered to determine whether or not a static defect is detectable by this pattern. Note that this way of representing training data looks like a Defect Detection Matrix used in cell-aware test pattern generation [22]. Besides training data, the Learning-Guided Intra-Cell Diagnosis (LGICD) module receives New Data.…”

Section: A Overall Diagnosis Flowmentioning

confidence: 99%

Cell-Aware Defect Diagnosis of Customer Returns Based on Supervised Learning

Mhamdi

Girard

Virazel

et al. 2020

IEEE Trans. Device Mater. Relib.

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…2, P1 to P4 denote static patterns (00, 01, 10, 11), and P5 to P16 denote dynamic patterns. This way of representing training data looks like a Defect Detection Matrix used in CA test pattern generation [20]. New data are composed of various instances.…”

Section: Previous Workmentioning

confidence: 99%

A Learning-Based Cell-Aware Diagnosis Flow for Industrial Customer Returns

Mhamdi

Girard

Virazel

et al. 2020

2020 IEEE International Test Conference (ITC)

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Diagnosis is crucial in order to establish the root cause of observed failures in Systems-on-Chip (SoC). In this paper, we present a new framework based on supervised learning for cellaware defect diagnosis of customer returns. By using a Naive Bayes classifier to accurately identify defect candidates, the proposed flow indistinctly deals with static and dynamic defects that may occur in actual circuits. Results achieved on benchmark circuits, as well as comparison with a commercial cell-aware diagnosis tool, show the effectiveness of the proposed framework in terms of accuracy and resolution. Moreover, the proposed flow has been experimented and validated on industrial circuits (two test chips and one customer return from STMicroelectronics), thus corroborating the results achieved on benchmark circuits.

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Defect-Location Identification for Cell-Aware Test

Cited by 15 publications

References 21 publications

Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Cell-Aware Defect Diagnosis of Customer Returns Based on Supervised Learning

A Learning-Based Cell-Aware Diagnosis Flow for Industrial Customer Returns

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