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

Safa Mhamdi 1 Arnaud Virazel 1 Patrick Girard 1 Alberto Bosio 2 Etienne Auvray 3 Aymen Ladhar 4 Eric Faehn 4
1 TEST - TEST
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 INL - CSH - INL - Conception de Systèmes Hétérogènes
INL - Institut des Nanotechnologies de Lyon
Abstract : 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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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02395493
Contributor : Patrick Girard <>
Submitted on : Wednesday, December 2, 2020 - 11:07:30 AM
Last modification on : Friday, June 4, 2021 - 5:46:01 PM
Long-term archiving on: : Wednesday, March 3, 2021 - 6:52:55 PM

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Safa Mhamdi, Arnaud Virazel, Patrick Girard, Alberto Bosio, Etienne Auvray, et al.. Towards Improvement of Mission Mode Failure Diagnosis for System-on-Chip. 25th International Symposium on On-Line Testing And Robust System Design (IOLTS), Jul 2019, Rhodes, Greece. pp.21-26, ⟨10.1109/IOLTS.2019.8854388⟩. ⟨lirmm-02395493⟩

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