All-optical analog comparator
Bibliographic record
Abstract
An analog comparator is one of the core units in all-optical analog-to-digital conversion (AO-ADC) systems, which digitizes different amplitude levels into two levels of logical '1' or '0' by comparing with a defined decision threshold. Although various outstanding photonic ADC approaches have been reported, almost all of them necessitate an electrical comparator to carry out this binarization. The use of an electrical comparator is in contradiction to the aim of developing all-optical devices. In this work, we propose a new concept of an all-optical analog comparator and numerically demonstrate an implementation based on a quarter-wavelength-shifted distributed feedback laser diode (QWS DFB-LD) with multiple quantum well (MQW) structures. Our results show that the all-optical comparator is very well suited for true AO-ADCs, enabling the whole digital conversion from an analog optical signal (continuous-time signal or discrete pulse signal) to a binary representation totally in the optical domain. In particular, this all-optical analog comparator possesses a low threshold power (several mW), high extinction ratio (up to 40 dB), fast operation rate (of the order of tens of Gb/s) and a step-like transfer function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".