Development of a Self-Monitored 3D Stress Sensor for Adhesive Degradation Detection in Multilayer Assemblies
Bibliographic record
Abstract
In this paper, a novel technique is proposed to provide a 3D piezoresistive MEMS stress sensor with a real-time self-monitoring of its bonding status. Perceiving the degradation of this interlayer due to different environmental parameters is crucial for reliable electronics and sensors assembly. The utilized sensor is featuring strain technology to fully extract the six stress components. Whenever a multilayer assembly, such as a sensor on a structure, is subjected to thermal or mechanical load, out-of-plane shear stress will accumulate at its edge as a reaction for peeling. As any degradation in the adhesion layer causes a significant reduction for out-of-plane shear stress, the capability of the 3D chip to measure this stress is employed to detect the degradation of the adhesive layer. To verify the capability of the current concept to experimentally quantify this loss, thermal energy is exploited for softening the bonding film. Losses up to 28 % for the out-of-plane shear stress are detected at a temperature equal to 80°c, which causes 71.9 percent decrease in the modulus of elasticity of the utilized adhesive. This significant correlation, between the out-of-plane shear stress loss and the bonding layer stiffness, is used to obtain a full picture of the adhesive deterioration in an early phase. The same technique can be utilized as a low profile detector for the debonding in multilayer electronics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".