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Effects of humidity, ionic contaminations and temperature on the degradation of silicone-based sealing materials used in microelectronics

2024· article· en· W4404666228 on OpenAlexaff
M. Yazdan Mehr, Pejman Hajipour, Mohammad Reza Karampoor, Henk van Zeijl, W.D. van Driel, Thierry Cooremans, F. De Buyl, G.Q. Zhang

Bibliographic record

VenueMicroelectronics Reliability · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsUniversity of Alberta
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekTechnische Universiteit DelftStichting voor Fundamenteel Onderzoek der Materie
KeywordsMicroelectronicsDegradation (telecommunications)HumiditySiliconeMaterials scienceIonic bondingComposite materialProcess engineeringChemical engineeringNanotechnologyElectrical engineeringChemistryEngineeringOrganic chemistryThermodynamicsIon

Abstract

fetched live from OpenAlex

This paper investigates the effects of three ageing factors (chemical, humidity, and temperature) and their interactions on the physical properties and degradation of silicone sealant used in microelectronic applications. The thermal degradation of silicone sealants was investigated by exposing samples to temperatures in the range of 150 up to 175 °C. Also, a set of samples were aged at 40 °C in a salt spray set-up with 100 % humidity in a salty atmosphere. Results showed detectable changes in the FTIR spectra of aged specimen as compared with the as-received sample. In all accelerated testing conditions, peak intensities decreased with ageing time, inferring that that the surface characteristics of the sealant is affected by ageing. Shear test results showed that with increasing the ageing time, the maximum shear stress in most cases has decreased in all ageing conditions. Also, it appears that samples with longer ageing times have experienced more elongation before failure. Results also show that salt spraying of specimens is associated with a decrease in the mechanical properties of the sealant, indicating the deleterious implications of ionic contaminations for the mechanical properties of samples. The design of the experiment was used to investigate the effects of three ageing factors and investigate their interactions on the physical properties of silicone rubber used in a particular industrial process. • In all accelerated testing conditions, peak intensities decreases with ageing time. It is an indication that the surface characteristics of the sealant is affected by thermal ageing and oxidized. • In the shear and tensile test it is shown that with increasing the ageing time, the maximum strength stress in all cases decreases at all ageing temperatures. • One of the observations in the mechanical test is that shear and tensile strength of 1 % Al2O3 mixture have best strength among all the conditions, and alkali environment has shown the decreasing trend of ageing. • Results showed that nanoparticles with 3 % Al2O3 have negative impact on the sealant as they adversely influence the flowability/viscosity of the composite mixture.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.006
GPT teacher head0.233
Teacher spread0.226 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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