Efficient Variability Analysis of Photonic Circuits by Stochastic Parametric Building Blocks
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a method to build stochastic parametric building blocks to be used in photonic process design kits. The building blocks are based on parametric macro-models computed by means of the generalized Polynomial Chaos Expansion technique. These macro-models can be built upfront and stored in process design kits. Being parametric, they do not have to be recalculated if the value of their design or statistical parameters change. It is shown that a single deterministic simulation performed with a classical circuit simulator is sufficient to perform the statistical analysis of any arbitrary photonic circuit realized combining these building blocks with different parameters, without the need of time-consuming Monte Carlo approach. Relevant numerical examples are used to demonstrate that the proposed macro-models are truly parametric, inherently stochastic and have greater simulation efficiency compared to Monte Carlo.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it