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Record W4402572819 · doi:10.1002/aelm.202400093

Combinatorial Optimization of Metal‐Insulator‐Insulator‐Metal (MIIM) Diodes With Thickness‐Gradient Films via Spatial Atomic Layer Deposition

2024· article· en· W4402572819 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Electronic Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaPrince Sattam bin Abdulaziz University
KeywordsMaterials scienceMetal-insulator-metalAtomic layer depositionInsulator (electricity)MetalDiodeLayer (electronics)Deposition (geology)OptoelectronicsNanotechnologyMetallurgyElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Metal‐insulator‐insulator‐metal (MIIM) diodes with thickness‐gradient films for the insulator layers are fabricated for the first time. Spatially varying atmospheric‐pressure chemical vapor deposition is used to deposit ZnO and Al 2 O 3 films with orthogonal gradient directions, producing 414 MIIM diodes with 414 different ZnO/Al 2 O 3 film‐thickness combinations on a single substrate for combinatorial and high‐throughput optimization. The nm‐scale ZnO/Al 2 O 3 films are printed in only 2 min and the entire device fabrication takes 7 h, which is much less than conventional approaches for investigating many insulator‐thickness combinations. Rapid identification of the optimal thickness combination is demonstrated; high‐performance diodes (asymmetry = 227, nonlinearity = 13.1, and responsivity = 12 A/W) are observed when a trap‐assisted tunneling mechanism is dominant for insulator thicknesses of 3.4–4.4 nm (ZnO) and 7.4 nm (Al 2 O 3 ).

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 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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.208
Teacher spread0.204 · 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