Publication:

Advanced RTN Analysis on 3D NAND Trench Devices using Physics-Informed Machine Learning Framework

Date

2025-01-01
Proceedings Paper
https://doi.org/10.1109/IRPS48204.2025.10983878
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dc.contributor.authorHigashi, Yusuke
dc.contributor.authorVaranasi, Anirudh
dc.contributor.authorRoussel, Philippe
dc.contributor.authorSaraza Canflanca, Pablo
dc.contributor.authorBastos, Joao
dc.contributor.authorGrill, Alexander
dc.contributor.authorCatapano, Edoardo
dc.contributor.authorAsanovski, Ruben
dc.contributor.authorFranco, Jacopo
dc.contributor.authorKaczer, Ben
dc.contributor.authorVaisman Chasin, Adrian
dc.contributor.authorVerreck, Devin
dc.contributor.authorRamesh, Siva
dc.contributor.authorBreuil, Laurent
dc.contributor.authorArreghini, Antonio
dc.contributor.authorRachidi, Sana
dc.contributor.authorJeong, Yongbin
dc.contributor.authorVan den Bosch, Geert
dc.contributor.authorRosmeulen, Maarten
dc.contributor.authorDegraeve, Robin
dc.date.accessioned2026-03-16T15:32:58Z
dc.date.available2026-03-16T15:32:58Z
dc.date.createdwos2025-10-18
dc.date.issued2025-01-01
dc.description.abstract3D NAND Trench cells have been proposed for further cost reduction by achieving lateral scaling. However, further dimension scaling of the memory cells raises concerns about random telegraph noise (RTN). In this work, a physics-informed machine learning framework is applied to analyze RTN in 3D NAND Trench devices as well as gate-all-around devices. The trench device has fewer traps but with a stronger single trap impact than GAA, resulting in larger impact of total RTN. Additionally, Comphy and TCAD simulations are carried out to physically interpret the results, revealing the detrimental impact of the gate edge of the Trench structure.
dc.description.wosFundingTextThe authors gratefully acknowledge the contributions of Dr. D. Claes, Dr. M. Vandemaele, and Dr. J. Diaz Fortuny in imec. This work has been funded by imec's Industrial Affiliation Program on Storage Memory devices.
dc.identifier.doi10.1109/IRPS48204.2025.10983878
dc.identifier.isbn979-8-3315-0478-6
dc.identifier.issn1541-7026
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/58853
dc.language.isoeng
dc.provenance.editstepusergreet.vanhoof@imec.be
dc.publisherIEEE
dc.source.conferenceIEEE International Reliability Physics Symposium (IRPS)
dc.source.conferencedate2025-03-30
dc.source.conferencelocationMonterey
dc.source.journal2025 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM, IRPS
dc.source.numberofpages6
dc.title

Advanced RTN Analysis on 3D NAND Trench Devices using Physics-Informed Machine Learning Framework

dc.typeProceedings paper
dspace.entity.typePublication
imec.identified.statusLibrary
imec.internal.crawledAt2025-10-22
imec.internal.sourcecrawler
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