A Machine Learning-based Approach to Optimize Repair and Increase Yield of Embedded Flash Memories in Automotive Systems-on-Chip

Manzini, A; Inglese, Paolo; Caldi, L.; Cantero, Rosa; Carnevale, Gianpietro; Coppetta, M.; Giltrelli, M.; Mautone, N.; Irrera, Fernanda; Ullmann, R.; Bernardi, P.

doi:10.1109/ets.2019.8791529

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Machine Learning-based Approach to Optimize Repair and Increase Yield of Embedded Flash Memories in Automotive Systems-on-Chip

Cited by 3 publications

References 13 publications

A Survey and Recent Advances: Machine Intelligence in Electronic Testing

A Survey and Recent Advances: Machine Intelligence in Electronic Testing

Industrial best practice: cases of study by automotive chip- makers

Special Session – Machine Learning in Test: A Survey of Analog, Digital, Memory, and RF Integrated Circuits

Contact Info

Product

Resources

About