Depth perception from blurring-a neural networks based approach for automated visual inspection in VLSI wafer probing
Bibliographic record
Abstract
An approach to the determination of depth as a function of blurring for automated visual inspection in VLSI wafer probing is presented. There exists a smooth relationship between the degree of blur and the distance of a problem from a test pad on a VLSI chip. Therefore, by measuring the amount of blurring, the distance from contact can be estimated. The effect of blurring on a point-object is studied in the frequency domain, and a monolithic relationship is found between the degree of blur and the frequency content of the image. Fourier feature extraction, with its inherent property of shift-invariance, was utilized to extract significant feature vectors. These vectors contain information on the degree of blur, and hence the distance from the probe. Neural networks were employed to map these feature vectors onto the actual distances. The network was then used in the recall mode to linearly interpolate the distance corresponding to the significant Fourier features of a blurred image.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".