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EnglishNowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails.
A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip / Manzini, A.; Inglese, P.; Caldi, L.; Cantoro, R.; Carnevale, G.; Coppetta, M.; Giltrelli, M.; Mautone, N.; Irrera, F.; Ullmann, R.; Bernardi, P.. - ELETTRONICO. - (2019), pp. 1-6. ((Intervento presentato al convegno 2019 IEEE European Test Symposium (ETS) tenutosi a Baden-Baden, Germany nel 27-31 May 2019 [10.1109/ETS.2019.8791529].
| Titolo: | A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip ...area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails. | |
| ISBN: | 978-1-7281-1173-5 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| ets19-99.pdf | accepted | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| PUBLISHED-08791529.pdf | final paper | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2838563
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