Analysis and Modeling of Controlled Silicon Substrate Roughness for Silver-Based Backside Metallization in Power Electronics Packaging
Bibliographic record
Abstract
In this paper, the analysis of a controlled structuration approach of the silicon (Si) substrate surface roughness through standard acidic wet chemical etching is proposed for the first time, for silver-based backside metallization (BSM) in power electronics packaging applications. Periodically spaced circular openings with diameters and separation distances from 1 um to 5 um were patterned using maskless laser lithography and wet etched in a Si substrate with a standard acidic HNP (HF: HNO3:H3PO4) mixture for 30s. The etched cavities were characterized by scanning electron microscopy (SEM). The extracted etching parameters from SEM observations were used to implement simple analytical models for the estimation of the average arithmetic surface roughness Raand the normalized etching depth (with respect to the opening's diameter after etching) as a function of the circular openings' dimensions, separation distance and the corresponding underetch. Roughness values ranging from 165 nm to 555 nm were estimated depending on the design specifications. A good correlation was observed between the experimental and theoretical values of the normalized etching depth. The introduced approach allows a rapid estimation of the surface roughness after etching without the need for photoresist removal for profilometry or AFM measurements, which makes it suitable for both rapid prototyping as well as for additional etching cycles after SEM if needed.
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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 | 0.001 |
| 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".