Optimization and tolerance for an exit pupil expander with 2D grating as out-coupler
Bibliographic record
Abstract
In this report, a simulation method with an example of optimization and tolerance analysis for an exit pupil expander (EPE) system using 2D grating as out-coupler has been proposed and demonstrated. In this design process, the first step is to establish a simulation workflow to dynamically link a raytracing engine and a rigorous coupled wave analysis (RCWA) solver. The RCWA solver, provided by Ansys Lumerical, can accurately calculate optical response of grating in the EPE system by solving Maxwell’s equations. The raytracing engine, provided by Ansys Zemax OpticStudio, is used to evaluate the whole system’s performance, such as efficiency and uniformity. In this step, the strategy of caching and interpolating the data when linking the RCWA solver and the ray-tracing engine is the key to make the simulation process streamlined and efficient. The second step is to prepare the system for optimization and tolerance analysis. A parametric model of the grating is first constructed. The parameters of this grating model are further considered as a function of position on the waveguide, which means the grating shape varies at different positions. The coefficients of this function are then used as variables during optimization and tolerance analysis by Ansys optiSLang. In this step, the spatial uniformity and the efficiency on the eye box are optimized, and the effect of the imperfect grating geometry and material are investigated.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".