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Record W4319996604 · doi:10.1116/6.0002167

Systematic study of InP/InGaAsP heated plasma etching and roughness improvement for integrated optical devices

2023· article· en· W4319996604 on OpenAlexafffund
Kaustubh Vyas, Kashif M. Awan, Ksenia Dolgaleva

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2023
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsIndium phosphideMaterials scienceOptoelectronicsDry etchingEtching (microfabrication)Reactive-ion etchingPlasma etchingFabricationSemiconductorLaserPhosphideNanotechnologyGallium arsenideOpticsMetallurgy

Abstract

fetched live from OpenAlex

Indium Phosphide (InP) is one of the most widely commercialized III–V semiconductor materials for making efficient lasers operating in the O-band and C-band. It is also gaining significant attention as a material platform for passive integrated optical devices operating in the telecommunication wavelength range for optical communication networks and sensing. Fabrication of such devices requires a process of lithography for pattern writing followed by plasma etching for transferring the pattern into the semiconductor material. InP is one of the most difficult materials to etch due to the fact that the etching by-products (InClx) are not volatile at temperatures less than 150 °C. There have been some studies showing InP etching at lower temperatures and room temperatures. However, after carefully studying these processes using multiple plasma etching tools, we found that the claimed processes are not repeatable because of the low volatility of the by-products at room temperature. In this work, we demonstrate a systematic study of etching InP using methane-hydrogen-based chemistry at low temperatures (60 °C) and chlorine-based chemistry at high temperatures (190 °C).

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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