Modeling Fabrication Non-Uniformity in Chip-Scale Silicon Photonic Interconnects
Bibliographic record
Abstract
Silicon photonic interconnect (SPI) is a promising candidate for the communication infrastructure in multiprocessor systems-on-chip (MPSoCs). When employing SPIs with wavelength-division multiplexing (WDM), it is required to precisely match different devices, such as photonic switches, filters, etc, in terms of their central wavelengths. Nevertheless, SPIs are vulnerable to fabrication non-uniformity (a.k.a. process variations), which influences the reliability and performance of such systems. Understanding process variations helps develop system design strategies to compensate for the variations, as well as estimate the implementation cost for such compensations. For the first time, this paper presents a computationally efficient and accurate bottom-up method to systematically study different process variations in passive SPIs. Analytical models to study the impact of silicon thickness and waveguide width variations on strip waveguides and microresonator (MR)-based add-drop filters are developed. Numerical simulations are used to evaluate our proposed method. Furthermore, we designed, fabricated, and tested several identical MRs to demonstrate process variations. The proposed method is applied to a case study of a passive WDM-based photonic switch, which is the building block in passive SPIs, to evaluate its optical signal-to-noise ratio (OSNR) under different variations. The efficiency of our proposed method enables its application to large-scale SPIs in MPSoCs, where employing numerical simulations is not feasible.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".