MétaCan
Menu
Back to cohort
Record W4251804542 · doi:10.1149/ma2020-01161073mtgabs

(Invited) Effect of the Plasma Etching on InAsP/InP Quantum Well Structures Measured through Low Temperature Micro-Photoluminescence and Cathodoluminescence

2020· article· en· W4251804542 on OpenAlexaff
Jean-Pierre Landesman, Nebile Işık Göktaş, Ray LaPierre, Shahram Ghanad-Tavakoli, E. Pargon, Camille Petit-Étienne, Christophe Levallois, J. Jiménez, Shabnam Dadgostar

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCathodoluminescencePhotoluminescenceEtching (microfabrication)Materials scienceQuantum wellLuminescenceOptoelectronicsReactive-ion etchingTernary operationPlasma etchingPlasmaInductively coupled plasmaSpectral lineAnalytical Chemistry (journal)OpticsChemistryNanotechnologyLaser

Abstract

fetched live from OpenAlex

Plasma etching is widely used for the fabrication of photonic devices with InP, GaAs, and their ternary or quaternary compounds. Generally speaking, these processes are rather well controlled in terms of the geometry and morphology of the fabricated features. However, the microscopic interaction between the etching ions / radicals and the semiconductors have not been paid much attention [1]. In order to investigate this issue, we design and grow dedicated quantum well (QW) samples. The samples are grown on InP substrates and include a series of strained InAsP QWs with different As/P compositions [1]. The luminescence from these samples is very intense and the lines associated with each QW are very sharp at low temperature, as illustrated in figure 1. Due to the different As/P compositions, each line can be related to a specific QW. The QWs are grown at different depths below the surface (between 300 nm and 1 µm). Thus, by examining the changes in shape and position of each line after plasma etching, it is possible to probe the interactions at different depths. In addition, we can also record micro-photoluminescence (µPL) spectra at very low temperature using an optical cryostat [2]. By choosing the excitation wavelength in the near infra-red (1064 nm), we avoid the presence of the InP-related luminescence lines on the spectra. This allows us to also investigate the lateral effects that plasma etching can produce e.g. on narrow stripes. Our etching experiments are performed using an inductively coupled plasma reactor, and different kinds of etching chemistries. As an example, we illustrate in figure 2 the different interactions taking place when etching a stripe (width: 50 µm) with CH 4 /H 2 and Cl 2 /CH 4 /Ar. Figure 2 shows that the CH 4 /H 2 etching broadens the PL lines for all QWs, whereas the Cl 2 /CH 4 /Ar in contrast yields much sharper lines than on the reference un-etched sample (figure 1). We have also investigated the intensity variation for each line across the ridges, and observed quantitative differences for the different etching processes. In another set of experiments, similar samples were measured using low temperature cathodoluminescence, while simultaneously biasing the samples at different positive and negative voltages (bias applied between surface and bulk). Strong peak shifts and broadening were observed, related to a quantum confined Stark effect [3], indicating the presence of charged species after some of the etching processes. These charged species interact with the built-in electric field in our samples. A mechanism describing the interaction between the plasma and the semiconductor material will be discussed based on the different luminescence measurements and mappings performed. [1] J.P. Landesman, J. Jiménez, C. Levallois, F. Pommereau, C. Frigeri, A. Torres, Y. Léger, A. Beck and A. Rhallabi, J. Vac. Sci. Technol. A, 34, 041304-1 (2016). [2] C. M. Haapamaki, PhD dissertation, McMaster University (2012). [3] L. Vina, E. E. Mendez, W. I. Wang, L. L. Chang, and L. Esaki, J. Phys. C: Solid State 20, 2803 (1987). Figure 1

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)0.001
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 score0.015
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.218
Teacher spread0.205 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicSemiconductor Lasers and Optical DevicesFrench-language works237,207