Surface roughness estimation by 3D stereo SEM reconstruction
Bibliographic record
Abstract
Surface roughness is an important parameter to describe materials’ topography. This parameter has been widely studied and presents important tasks in many engineering applications. The development of non-contact-based roughness measurement techniques for engineering surfaces has received much attention. However, stylus-based equipments are still dominating this measurement task. Stylus techniques have great inherent limitations as they were originally intended to acquire 2D surface topography. Therefore, 3D surface roughness data can only be obtained from stylus equipment executing multiple scans of the surface. This task takes a lot of time to achieve a satisfactory result, may make micro-scratches on surfaces and can only evaluate a small area in a reasonable amount of time. In this work a new automated methodology for obtaining a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereo-vision is proposed. The 3D models can then be used to evaluate the surface roughness parameters. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. Since the brightness change of corresponding pixels is negligible for the small tilt involved in stereo SEM, and the cost function relies on dynamic programming, the matching algorithm uses a sum of absolute differences (SAD) over a variable pixel size window and an occlusion parameter which penalizes large depth discontinuities, that in practice, smooths the disparity map and the corresponding reconstructed surface. This step yields a disparity map, i.e. the differences between the horizontal coordinates of the matching points in the stereo images. The horizontal disparity map is finally converted into heights according to the SEM acquisition parameters: tilt angle, magnification and pixel size. A validation test was first performed using a microscopic grid with manufacturer specifications as reference. Finally, some surface roughness parameters were calculated within the model
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".