A comparison of switching energy of resonant and nonresonant electro-optic switches
Bibliographic record
Abstract
Optical space switching is an important functionality in dense wavelength division multiplexing (DWDM) optical communication systems, particularly within reconfigurable optical add-drop multiplexers (ROADMs) [1]. Current commercially available ROADMs are based on micro-electromechanical systems (MEMS) or liquid crystal switches but these do not have sufficient switching speed for future network requirements. Power consumption (i.e. energy per switching operation multiplied by switching rate) is a very important parameter in the selection of a switching technology. Space switches based on current injection in silicon have been reported with nanosecond switching speeds and average power consumption on the order of mW [2], which becomes significant if many switches are required in a fabric. Electro-optic (EO) switches, which utilize the Pockels effect in which the refractive index changes when an external voltage is applied [3], only dissipate power when the switch state is changed. Electro-optic switches can be implemented either as non-resonant designs (for example the Mach-Zehnder interferometer (MZI)) or as resonant designs (for example the Fabry Perot interferometer (FPI)). In this study we compare the switching energies of electro optic MZI and FPI switches by considering the capacitance of the switch, which is determined by the length of the active region of the switch. We show that for a non-resonant switch, switching energy increases linearly with device length, regardless of applied voltage, and so is simply determined by the strength of the electro-optic coefficient. We assume that the resonant switch is implemented as a switchable comb filter [4], with a free-spectral range equal to twice the wavelength spacing. This then fixes the interferometer length. As a result the resonant switch has requires significantly less switching energy for the same material parameters and is thus of interest for future ROADM implementations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".