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Degradation of silicone-based sealing materials used in microelectronics

2023· article· en· W4366140062 on OpenAlexaff
M. Yazdan Mehr, Pejman Hajipour, Henk van Zeijl, Walidabd El, Thierry Cooremans, F. De Buyl, Guoxiong Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of Alberta
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekTechnische Universiteit Delft
KeywordsMicroelectronicsMaterials scienceAdhesiveSiliconeSealantDurabilityComposite materialDegradation (telecommunications)Forensic engineeringComputer scienceNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Adhesive bonding is a key joining technology in many industrial applications, including automotive, aerospace industries, biomedical devices, and microelectronic components. Adhesive bonding is gaining more and more attention due to the increasing demand for joining similar or dissimilar components, mostly within the framework of designing lightweight structures. Silicone sealant is widely used in engineering application due to its thermal stability, excellent energy absorption, and good damping characteristics. In those applications, sealant usually exposed to various environment stress, such as, high temperature, mechanical stress, humidity, light radiation, and chemical attack. Long-term stability and durability of sealant is crucial to the performance of the associated application. The main degrading factors for silicone in microelectronic applications are temperature, humidity, alkali, and mechanical loading. The focus in the present paper is to understand different failure mechanisms in silicone sealants and adhesives and to study how different environmental, mechanical, and service-related stresses attribute to the kinetics and extent of degradation in silicone sealants and adhesives. The impact of different failure mechanisms on the lifetime and reliability of microelectronic devices will be methodically investigated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.334

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 head0.011
GPT teacher head0.225
Teacher spread0.213 · 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
Published2023
Admission routes1
Has abstractyes

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