2022 IEEE International Memory Workshop (IMW) 2022
DOI: 10.1109/imw52921.2022.9779277
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Endurance improvements and defect characterization in ferroelectric FETs through interface fluorination

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Cited by 16 publications
(13 citation statements)
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“…As they cannot easily detrap, compared to SiON, a memory closure is observable, as these charges will pin domains and shift the internal bias field. This effect has recently been explored as well for fluorinated interfaces, reporting consistent results with the here presented data.…”
Section: Resultssupporting
confidence: 90%
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“…As they cannot easily detrap, compared to SiON, a memory closure is observable, as these charges will pin domains and shift the internal bias field. This effect has recently been explored as well for fluorinated interfaces, reporting consistent results with the here presented data.…”
Section: Resultssupporting
confidence: 90%
“…31−33 Numerous efforts have been made to reduce the impacts of such nonidealities from the device process, and a circuit point of view. [34][35][36][37][38][39]23,25 Previously, it has been reported how the quality of the interface and the READ-Voltage play a pivotal role in the performance of FeFETs, especially for low-frequency noise response, retention, and endurance. 40,41,25,21,42−44 In this work, we aim to maximize the reliability and performance of FeFETs by adopting a synergistic approach of READ-voltage optimization and interfacial-layer engineering.…”
Section: ■ Introductionmentioning
confidence: 99%
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“…These changes in both cases can be based on interface defects and occur especially after a high number of cycles. An in-depth analysis and detailed understanding of the defect behaviour of the ferroelectric layer and the interface can be found elsewhere [79]. Summarized, it is a trade of between a better S th S or a better µ ef f performance, that means to avoid mobility variations as well as a significant change of the S th S is a combination of the pulse width and amplitude change in programming the devices.…”
Section: Characterization Of Fetft-based Neuromorphic Devicesmentioning
confidence: 99%