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Energy Band Engineering of Metal Nanodots for High Performance Nonvolatile Memory Application

2011· article· en· W2139462396 on OpenAlexaff
Yan Pei, Tatsuro Hiraki, Toshiya Kojima, Takafumi Fukushima, Mitsumasa Koyanagi, Tetsu Tanaka

Bibliographic record

VenueKey engineering materials · 2011
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsHatch (Canada)
FundersJapan Society for the Promotion of Science
KeywordsNanodotMaterials scienceNon-volatile memoryOptoelectronicsWork functionLayer (electronics)Work (physics)Metal gateNanotechnologyElectrical engineeringTransistorGate oxideEngineering

Abstract

fetched live from OpenAlex

In this work, high density and small size metal nanodots (MND) with different work-functions were fabricated as a floating gate of nonvolatile memory (NVM) devices by self-assembled nanodot deposition (SAND). The energy band engineering of NVM was demonstrated through controlling MND work-function. For single MND layer floating gate NVM, the retention time was improved by choosing high work-function MND. Furthermore, we proposed a new type NVM with a double stacked MND floating gate. Here, the high work-function MND are placed on the top layer and the low work-function MND are placed on the bottom layer. A large memory window and long retention time were obtained. However, the thermal electron excitation is dominant for the electron discharge process during retention. How to reduce the defects in MND layer is important for further improving of memory characteristics.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.168
Teacher spread0.158 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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