Lens parameters optimization sensitivity prediction using Ansys Zemax multi-configuration composite feature
Bibliographic record
Abstract
The merit function defines the permissible range of component variable values in optimizing lens systems. This ensures the optimization algorithm explores parameter variations within specified bounds, contributing to the generation of feasible designs. In this study, we introduce an approach to identify the optimization-sensitive surface parameters of a relay lens through the utilization of the multi-configuration composite feature. The parameter variations sensitivity is analyzed by employing the Zernike Standard Sag Surface as an add-on composite surface, with a perturbation pattern of spherical aberration irregularity across multiple configurations preceding each lens surface within the Zemax lens data editor. The primary performance degradation impact on surface parameters is identified by examining the image spot dimension charts. In light of the analysis results, rigorous constraints are imposed on the sensitive component. A suitable variable range is defined to establish practical limits, aiding the algorithm in searching for solutions within the feasible parameter space. This ensures optimized designs that are physically realizable and meet the specified performance criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".