Hierarchical Memory Diagnosis

Hierarchical Memory Diagnosis

Cardoso Medeiros, G. (TU Delft Quantum & Computer Engineering)
Fieback, M. (TU Delft Computer Engineering)
Gebregiorgis, A.B. (TU Delft Computer Engineering)
Taouil, M. (TU Delft Computer Engineering)
Poehls, L. B. (Rheinisch-Westfälische Technische Hochschule)
Hamdioui, S. (TU Delft Quantum & Computer Engineering)

Quantum & Computer Engineering

2022

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are diagnosed hierarchically; first, their location, then their nature (i.e., static or dynamic), and finally, their functional fault model. The HMD approach leads to a more accurate diagnostic, enabling the precise identification of yield loss causes.

Algorithm
Diagnosis
Fault
Memory
Test

http://resolver.tudelft.nl/uuid:2838d508-7ed5-4fab-ad62-001e57e9f1b9

https://doi.org/10.1109/ETS54262.2022.9810467

IEEE

2023-07-01

9781665467063

Proceedings - 2022 IEEE European Test Symposium, ETS 2022

27th IEEE European Test Symposium, ETS 2022, 2022-05-23 → 2022-05-27, Barcelona, Spain

Proceedings of the European Test Workshop, 1530-1877, 2022-May

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 G. Cardoso Medeiros, M. Fieback, A.B. Gebregiorgis, M. Taouil, L. B. Poehls, S. Hamdioui