Dynamic Modeling of Stress-Induced Defect Expansion in VCSELs
Bibliographic record
Abstract
Many failures of semiconductor-based oxide confined vertical cavity surface emitting lasers (VCSELs) are closely related to the generation and expansion of defects in the device structure. However, existing research has predominantly focused on the static study of defect morphology, with little attention given to analyzing the dynamic process of defect expansion, which limited our ability to predict device random failures due to lack of understandings on defect generation and expansion. To address this issue, we present a macroscopic phenomenological evolution model that describes the dynamic expansion of defects in VCSELs, in which the expansion of defects is treated as an anisotropic lattice strain diffusion process. We further exploit a diffusion-limited aggregation (DLA) method in solving the diffusion equation, which describes the random propagation and aggregation of strain in the vicinity of highly strained areas, resulting in defect formation when the stress from accumulated strain surpasses the bonding force of the atoms in the lattice. Our simulation result manages to replicate the dendritic expansion morphology of defects, aligning with experimental observations very well. Our model also predicts an accelerated defect expansion process, which is again consistent with the experimental result. This model finds relevance in applications such as random failure prediction through device aging, burn-in condition setting in device screening, and device structural and/or material design improvement for mitigating defect expansion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".