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Record W3185843700 · doi:10.1149/ma2021-0121857mtgabs

Stress Engineering of Dielectric Films on Semiconductor Substrates

2021· article· en· W3185843700 on OpenAlexaff
Brahim Ahammou, Aysegul Abdelal, Solène Gérard, Christophe Levallois, Peter Mascher, Jean-Pierre Landesman

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceDielectricResidual stressPlasma-enhanced chemical vapor depositionThin filmAmorphous solidOptoelectronicsSemiconductorRefractive indexBand gapComposite materialWaferEllipsometryChemical vapor depositionNanotechnologyCrystallography

Abstract

fetched live from OpenAlex

Dielectric thin films deposited by plasma enhanced chemical vapor deposition (PECVD) have been extensively studied over the last decades due to their interesting optical and electrical properties besides their many applications in microelectronic and optoelectronic devices. Recently published studies have shown the impact of the mechanical properties of amorphous dielectric films on semiconductor substrates [1]. In strain engineering, stressed films are used to control on demand the physical properties of semiconductors at the surface such as bandgap energy, dielectric constant, and refractive index. We aim to study in this work how to control the distribution of the strain field beneath a dielectric film and how the changing of the residual stress affects the physical properties of the dielectric film itself. We deposited hydrogenated amorphous silicon nitride a-SiN:H films on Si, InP, and GaAs substrates using a capacitively coupled plasma reactor CCP-PECVD with a radiofrequency (RF) power at 13.56 MHz.The a-SiN:H films were deposited at 280 °C, with a thickness of approximatively 500 nm, using a SiH4/NH3/N2/Ar precursor mixture. The RF power injected into the plasma allows a tunable residual stress and a wide range of built-in stress, from tensile (+ 300 MPa) to compressive (– 400 MPa). To evaluate the residual stress in our deposited thin films, we used the standard method of wafer curvature measurements. The thickness and the refractive index were characterized by variable angle spectroscopic ellipsometry (VASE). The determination of Young’s modulus and hardness of the a-SiN:H films was performed by nanoindentation. We noticed that the adjustment of the residual stress leads to the modification of the film in terms of optical and mechanical properties. In order to investigate the deformation induced in the semiconductor, an understanding of the semiconductor mechanical behavior on a microscopic scale is required. Thus, we performed a detailed investigation of the effect of strain on the degree of polarization (DOP) of the photoluminescence signal on direct bandgap substrates [2]. After examining the DOP profiles beneath the film, it is interesting to note that the anisotropic deformation extends to significant depths (~ 8 µm), as illustrated in figure 1, while the horizontal distribution of the stress can propagate beyond the edge of the sample by a few microns (see figure 2). The confinement of light in some photonic devices such as photoelastic planar waveguides can be achieved by a photo-elastic effect in semiconductor using stressed dielectric films [3]. [1] S. Gérard et al., “Photoluminescence mapping of the strain induced in InP and GaAs substrates by SiNx stripes etched from thin films grown under controlled mechanical stress,” Thin Solid Films, vol. 706, p. 138079, Jul. 2020, doi: 10.1016/j.tsf.2020.138079. [2] D. T. Cassidy, C. K. Hall, O. Rehioui, and L. Bechou, “Strain estimation in III-V materials by analysis of the degree of polarization of luminescence,” Microelectron. Reliab., vol. 50, no. 4, pp. 462–466, Apr. 2010, doi: 10.1016/j.microrel.2009.11.003. [3] P. A. Kirkby, P. R. Selway, and L. D. Westbrook, “Photoelastic waveguides and their effect on stripe-geometry GaAs/Ga 1-xAlxAs lasers,” J. Appl. Phys., vol. 50, no. 7, pp. 4567–4579, Jul. 1979, doi: 10.1063/1.326563. Figure 1

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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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueECS Meeting AbstractsSame topicSemiconductor materials and devicesFrench-language works237,207