Correlation of local strain and temperature measurements in confocal Raman microscopy
Bibliographic record
Abstract
Abstract In microsystem technologies, it is very common to employ Raman spectroscopy to monitor the strain generated during the fabrication of microelectromechanical systems and integrated circuits devices, an example being through‐silicon vias. Typically, for strain analysis, the laser intensity is chosen to be sufficiently low to avoid the laser heating affecting the position of the Raman lines under consideration, because theoretically, the measured strain can have two origins: for one as a consequence of mechanical stress through Hooke's law, and for the other, as a consequence of temperature. The latter has often been overlooked throughout literature. Here, we revisit a couple of cases, for which tensile strain is detected, by comparing them to Raman analysis on samples of empty through‐silicon vias, that were expected to be strain free. By simultaneously monitoring strain and the local temperature through the ratio of anti‐Stokes to Stokes lines, it is found that at least part of the strain usually attributed to mechanical stress can be quantitatively attributed to laser‐induced thermal expansion, even at relatively low laser intensities. We support our findings using a finite element model and present key implications on the interpretation of strain measurements in copper‐filled through‐silicon vias.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".