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On the Origin of Holes During Polarization Reset in Floating Body Ferroelectric FETs Towards Improving Switching Efficiency

2024· article· en· W4407692703 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFerroelectric and Negative Capacitance Devices
Canadian institutionsEMD Inc. (Canada)
FundersU.S. Air ForceU.S. Department of EnergyNational Science Foundation
KeywordsFerroelectricityReset (finance)Materials scienceOptoelectronicsPolarization (electrochemistry)DielectricChemistryBusiness

Abstract

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In this work, we performed a comprehensive combined experimental and modeling study on the polarization reset mechanisms of floating body (i.e., channel) ferroelectric FETs, an important class of device with growing interests due to added functionalities and improved reliabilities. Using fully-depleted silicon-on-insulator (FDSOI) FeFET as a classical example, we demonstrate that: 1) without hole generation mechanisms, floating body FeFETs during reset is simply a capacitor divider, with negligible ferroelectric voltage drop for switching; ii) Band-to-band-tunneling (BTBT) around gate-to-S/D overlap even with zero drain bias generates holes to facilitate the reset in FDSOI FeFET, though at a slower speed and hold the reset state; iii) With scaling, S/D inner fringe field can enable fast reset, thus offering a potential efficiency boost approach; iv) a compact FDSOI FeFET model is developed that can capture the BTBT effect and reproduce the observed behaviors; v) the reset mechanism is also validated in a NAND string composed of FDSOI FeFETs, demonstrating its relevant applications. These insights show the strategies in improving reset efficiency, i.e., enhanced BTBT and inner fringe field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it