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Record W2751345945 · doi:10.1149/2.0111711jss

Low Source/Drain Contact Resistance for AlGaN/GaN HEMTs with High Al Concentration and Si-HP [111] Substrate

2017· article· en· W2751345945 on OpenAlexaff
S. J. Duffy, B. Benbakhti, M. Mattalah, Weidong Zhang, Meriem Bouchilaoun, M. Boucherta, K. Kálna, N. Bourzgui, Hassan Maher, A. Soltani

Bibliographic record

VenueECS Journal of Solid State Science and Technology · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsOhmic contactMaterials scienceContact resistanceOptoelectronicsDiffusion barrierBarrier layerAnnealing (glass)Etching (microfabrication)FabricationSubstrate (aquarium)Layer (electronics)NanotechnologyComposite material

Abstract

fetched live from OpenAlex

An optimized fabrication process of ohmic contacts is proposed to reduce the source/drain access resistance (R C ) and enhance DC/RF performance of AlGaN/GaN HEMTs with a high Al concentration. We show that source/drain R C can be considerably lowered by (i) optimally etching into the barrier layer using Ar + ion beam, and by (ii) forming recessed contact metallization using an optimized Ti/Al/Ni/Au (12 nm/200 nm/40 nm/100 nm) multilayers. We found that a low R C of ∼0.3 Ω.mm can be achieved by etching closer to the 2-Dimensional Electron Gas (2DEG) at an optimum etching depth, 75% of the barrier thickness, followed by a rapid thermal annealing at 850°C. This is due to the very small distance between the alloy and the 2DEG (higher electric field) as shown by 2D drift-diffusion simulations combined with Transmission Line Model (TLM) extractions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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Same venueECS Journal of Solid State Science and TechnologySame topicGaN-based semiconductor devices and materialsFrench-language works237,207