Microscopic to macroscopic modelling of optical defect inspection system
Bibliographic record
Abstract
Optical inspection systems allow faster detection of defects on semiconductor wafers than scanning electron microscopy (SEM) inspection systems. However, optical detection becomes more challenging as the structure feature size shrinks below the optical diffraction limit with the advancement of technology nodes in semiconductor manufacturing. To overcome this challenge and achieve optimal performance, the optical system must be tailored to the specific characteristics of the wafer sample which requires knowledge of the underlying microscopic and macroscopic optical phenomena. In this work, we proposed a multiphysics simulation workflow to model the microscopic light interaction with the wafer sample using Ansys Lumerical FDTD and the macroscopic optics of the inspection system using Ansys Zemax OpticStudio. The optimum optical system design with maximum defect signal strength could be achieved through defect image analysis. Together, FDTD and OpticStudio facilitate the design of complex optical inspection systems and reduce the cycle time for creating inspection recipes in the development of advanced technology nodes in semiconductor manufacturing.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".