Efficient layout-aware statistical analysis for photonic integrated circuits
Bibliographic record
Abstract
Fabrication variability significantly impacts the performance of photonic integrated circuits (PICs), which makes it crucial to quantify the impact of fabrication variations before the final fabrication. Such analysis enables circuit and system designers to optimize their designs to be more robust and obtain maximum yield when designing for manufacturing. This work presents a simulation methodology, Reduced Spatial Correlation Matrix-based Monte-Carlo (RSCM-MC), to efficiently study the impact of spatially correlated fabrication variations on the performance of PICs. First, a simple and reliable method to extract physical correlation lengths, variability parameters that define the inverse of the spatial frequencies of width and height variations over a wafer, is presented. Then, the process of generating correlated variations for MC simulations using RSCM-MC methodology is presented. The methodology generates correlated variations by first creating a reduced correlation matrix containing spatial correlations between all the circuit components, and then processing it using Cholesky decomposition to obtain correlated variations for all circuit components. These variations are then used to conduct MC simulations. The accuracy and the computation performance of the proposed methodology are compared with other layout-dependent Monte-Carlo simulation methodologies, such as Virtual wafer-based Monte-Carlo (VW-MC). A Mach-Zehnder lattice filter is used to study the accuracy, and a second-order Mach-Zehnder filter and a 16x16 optical switch matrix system are used to compare the computational performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".