Development of numerical simulation model for temperature gradient transient liquid phase bonding with concentration dependent diffusion coefficient
Bibliographic record
Abstract
A new numerical model that can be used to study the kinetics of temperature gradient transient liquid phase (TG -TLP) bonding under concentration-dependent diffusivity has been developed by using a first-order implicit-explicit numerical method, and Landau coordinate transformation with adaptable spatial discretization. A number of non-trivial assumptions that reduce accuracy are avoided and the model is validated with experimental data reported in the literature. In contrast to previously reported findings, the results of this new model show that solid-state diffusion plays a significant role, not only in controlling the transition in solidification behavior from bidirectional to unidirectional, but also affects the kinetics of the bonding process. Previous reports have stated that TG-TLP bonding always produces a shorter bonding completion time compared to the conventional transient liquid phase bonding (C -TLP) due to a higher solute diffusivity in the liquid compared to the solid. However, the results in this work show that the concentration gradient in the liquid is the major factor that enhances the solidification kinetics in TG -TLP bonding to produce shorter bonding time. Furthermore, in C -TLP bonding, increase in temperature above a specific threshold temperature can result in a longer bonding completion time. However, a detailed analysis in this study shows that this undesirable behaviour can be prevented in TG -TLP bonding, if the concentration gradient in the liquid facilitates adequate solidification kinetics to overcome the increased liquid volume that normally accompanies increased bonding temperature. Finally, it is often assumed that during TG-TLP bonding, after the commencement of unidirectional solidification, which helps to produce desirable directionally solidified single crystal joint, the solidification mode occurs persistently till the end of the bonding process. Nevertheless, the analysis performed in this work shows that the occurrence of unidirectional solidification during TG -TLP bonding can be compromised, by reverting to bidirectional solidification, if a single heat source is used to impose the temperature gradient, depending on the location of the heat source relative to the joint region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".