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Record W2800631179 · doi:10.7939/r38k7579d

Theoretical and Experimental Investigation on the Use of Surface Acoustic Wave Sensors for Evaluating the Adhesion of SU-8 Thin Films

2015· article· en· W2800631179 on OpenAlexaboutno aff
EL Gowini

Bibliographic record

VenueUniversity of Alberta Library · 2015
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSurface acoustic waveAdhesionAcousticsMaterials scienceSurface (topology)Thin filmComposite materialPhysicsNanotechnologyMathematics

Abstract

fetched live from OpenAlex

This research investigates the use of a SU-8/AlN/Si SAW sensor for evaluating the adhesion of SU-8 thin films. A theoretical model is developed to plot the wave dispersion profile for the SU-8/AlN/Si configuration. A spring interface model is utilized to represent the SU-8/AlN interface using a series of massless springs with stiffness K (N/m^3). The value of the interface spring stiffness K is changed to represent different levels of SU-8 adhesion. The wave dispersion profiles for the intermediate adhesion levels are plotted using the theoretical model. The change in wave velocity due to the change in adhesion of the SU-8 thin film is evaluated. The sensitivities of different configurations of the SU-8/AlN/Si SAW sensors are investigated. Four SAW sensor designs are selected to evaluate the adhesion of the SU-8 thin film. The four SAW sensors operate in the frequency range of 84-208MHz. A process flow for fabricating the SAW sensors at the University of Alberta micro-and nanofabrication facility “nanofab” is developed. The fabricated sensors are packaged using wire bonding to allow measurement of their frequency responses using a Vector Network Analyzer. For each of the four SAW sensor designs two sensor configurations will be developed. In one configuration the SU-8 film will be patterned on top of a thin gold film on the surface of the AlN/Si layers and in the second configuration the gold film will be coated with an Omnicoat layer prior to patterning the SU-8 film. Omnicoat is an adhesion promoter that is widely used to improve the adhesion of SU-8 to gold. The frequency responses from both sensor configurations are measured for each of the four SAW sensor designs and the frequency shift is evaluated. The frequency shift illustrates the change in adhesion of the SU-8 film with and without omnicoat. The phase velocity values also shift to a higher value for the sensor configurations without omnicoat. As the adhesion of the SU-8 film drops in the absence of omnicoat the stresses transferred to the SU-8 layer drop and accordingly the wave propagation is concentrated in the AlN/Si layers, which have a higher wave velocity than SU-8. However, in the presence of omnicoat the adhesion of the SU-8 layer improves and the surface acoustic wave propagates in the SU-8 layer, which leads to a drop in the phase velocity. The theoretical model is used to find the equivalent interface spring stiffness values for the two cases of SU-8 adhesion i.e. with and without omnicoat. This is accomplished by curve fitting the dispersion curves to the phase velocity values for from both sensor configurations. The equivalent interface spring stiffness values are found to be 8.0992 x10^9 N/m^3 and 7.9492x10^9 N/m3 for the SAW sensors with omnicoat and without omnicoat, respectively. These values indicate that when omnicoat is used as an adhesion promoter for SU-8 the interface spring stiffness increases due to the improved adhesion. However, without omnicoat the adhesion of the SU-8 layer drops, which corresponds to the lower interface spring stiffness value.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.056
GPT teacher head0.223
Teacher spread0.167 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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