Advancements in diagnosis driven yield analysis (DDYA): A survey of state-of-the-art scan diagnosis and yield analysis technologies

Huang, Yu; Wu, Yang; Cheng, Wu-Tung

doi:10.1109/ets.2015.7138758

Cited by 14 publications

(5 citation statements)

References 52 publications

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“…In [17], authors improve on-chip diagnosis resolution with a modified k-nearest neighbors classifier that is updated with real-time failure data. In [18], volume diagnosis resolution is improved with a Bayesian classifier that identifies the actual candidates based on their layout properties. In [19], authors present a novel yield optimization methodology based on establishing a strong correlation between a group of fails and an adjustable process parameter.…”

Section: State Of the Art And Motivationsmentioning

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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Section: State Of the Art And Motivationsmentioning

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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“…In [16], authors improve on-chip diagnosis resolution with a modified k-nearest neighbors classifier that is updated with real-time failure data. In [17], volume diagnosis resolution is improved with a Bayesian classifier that identifies the actual candidates based on their layout properties. In [18], authors present a novel yield optimization methodology based on establishing a strong correlation between a group of fails and an adjustable process parameter.…”

Section: State Of the Art And Motivationsmentioning

confidence: 99%

Towards Improvement of Mission Mode Failure Diagnosis for System-on-Chip

Mhamdi

Virazel

Girard

et al. 2019

2019 IEEE 25th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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In critical (e.g. automotive) applications, Systems-on-Chip (SoC) failures that occurred during mission mode (in the field) are the most critical since they may lead to catastrophic effects. In this context, diagnosis is crucial in order to establish the root cause of observed failures with the best accuracy. With the advent of very deep submicron technologies (i.e. 7 nm), achieving such level of accuracy will become more and more difficult with today's intra-cell diagnosis tools based on effectcause or cause-effect paradigms. This will compromise the success of subsequent Physical Failure Analysis (PFA) done on defective SoCs. Machine Learning (ML) is now used in numerous classification problems where the knowledge on some data can be used to classify a new instance of such data. In particular, several ML-based solutions exist to address volume diagnosis for yield improvement. These learning-guided diagnosis approaches start from an existing set of defect candidates and try to minimize this set (eliminate bad candidates) owing to the use of ML tools and numerous data collected during production test (e.g. thousands of failed chips with candidates correctly labeled). Although efficient in volume diagnosis, these approaches cannot be used to identify the root cause of failures in customer returns, since only one failed chip is investigated in this case, with no information about the defective behavior of some other similar chips used in the same conditions (environment, workload, etc.). In this paper, we propose a new learning-guided approach for diagnosis of mission mode failures in customer returns. The proposed approach directly produces a minimum set of good candidates derived from the application of the learning-guided intra-cell diagnosis flow. Results obtained on a set of benchmark circuits, and comparison with a commercial intra-cell diagnosis tool, show the feasibility, effectiveness and accuracy of the proposed approach.

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“…Especially with the advent of very deep submicron technologies (i.e., 7 nm), a high resolution (very few or one candidate) is not always reachable by existing CA logic diagnosis tools based on conventional methods [8]. For this reason, considerable efforts have been spent on improving resolution by using machine learning techniques, mainly through the extraction of features that allow correct candidates (those that correctly represent defect locations) to be distinguished from incorrect ones [2,4,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

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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Advancements in diagnosis driven yield analysis (DDYA): A survey of state-of-the-art scan diagnosis and yield analysis technologies

Cited by 14 publications

References 52 publications

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

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

Towards Improvement of Mission Mode Failure Diagnosis for System-on-Chip

Defect Diagnosis Techniques for Silicon Customer Returns

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