THE EFFECT OF THE GRANULOMETRIC COMPOSITION OF THE FILLER AND HEAT TREATMENT ON THE ADHESION STRENGTH OF MULTILAYER METAL COATING ON AlSiC MMС’S SURFACE
Bibliographic record
Abstract
Background. Modern semiconductor power devices (SPD) contain temperature compensators (TC) in their design, which can be made of a metal matrix composite material based on an aluminum matrix alloy and silicon carbide micropowder (MMC AlSiC). A multilayer metal coating is applied to the TC surface, which makes it possible to firmly connect the TC with an active semiconductor crystal in the SPP. The adhesion strength of this coating to the surface of MMCM AlSiC largely determines the reliability of the SPP. The aim of this work is to study the effect of the granulometric composition of the filler and heat treatment on the adhesion strength of multilayer Al-Ti-Ni-Ag metal coatings on the surface of AlSiC MMKM. Materials and methods. The studied samples of MMC AlSiC based on the AK9 aluminum matrix alloy were prepared by the method of vacuum-compression impregnation. As a filler, silicon carbide micropowders of grain size distribution F120, F150, F180 and mixtures F120 + M10P (10 %), F150 + M10P (10 %), F180 + M10P (10 %) were used. A four-layer metal coating (Al-Ti-Ni-Ag) was applied to the surface of the studied samples of MMCM AlSiC by magnetron sputtering. The adhesion strength of the bond between the coating and the composite surface was determined by the peeling method. Results. The adhesive strength of a multilayer metal coating on the surface of MMC AlSiC samples with different grain-size composition of SiC filler was measured depending on the duration and temperature of annealing in an atmosphere of hydrogen and argon. Conclusions. Annealing the samples under study in a hydrogen or argon atmosphere for more than 30 min at a temperature of 450 °C (or more than 60 min at a temperature of 350 °C) leads to a significant (almost threefold) increase in the adhesion strength of the metal coating.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".