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Record W2990680884 · doi:10.1109/led.2019.2956346

Fabrication and Modeling of pn-Diodes Based on Inkjet Printed Oxide Semiconductors

2019· article· en· W2990680884 on OpenAlexaff
Gabriel Cadilha Marques, Mehdi B. Tahoori, Jasmin Aghassi‐Hagmann, Adrianus Matthew Sukuramsyah, August Arnal, Sami Bolat, Abdessalem Aribia, Xiaowei Feng, Surya Abhishek Singaraju, Eloi Ramón, Yaroslav E. Romanyuk

Bibliographic record

VenueIEEE Electron Device Letters · 2019
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceDiodeOptoelectronicsSemiconductorOxideMicroelectronicsThin-film transistorTransistorNanotechnologyElectrical engineeringVoltageLayer (electronics)Metallurgy

Abstract

fetched live from OpenAlex

Oxide semiconductors have the potential to increase the performance of inkjet printed microelectronic devices such as field-effect transistors (FETs), due to their high electron mobilities. Typical metal oxides are n-type semiconductors, while p-type oxides, although realizable, exhibit lower carrier mobilities. Therefore, the circuit design based on oxide semiconductors is mostly in n-type logic only. Here we present an inkjet printed pn-diode based on p- and n-type oxide semiconductors. Copper oxide or nickel oxide is used as p-type semiconductor whereas n-type semiconductor is realized with indium oxide. The measurements show that the pn-diodes operate in the voltage window typical for printed electronics and the emission coefficient is 1.505 and 2.199 for the copper oxide based and nickel oxide based pn-diode, respectively. Furthermore, a pn-diode model is developed and integrable into a circuit simulator.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2019
Admission routes1
Has abstractyes

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Same venueIEEE Electron Device LettersSame topicZnO doping and propertiesFrench-language works237,207