Automated laser trimming for ultralow error function GFF
Bibliographic record
Abstract
Gain flatness of optical amplifiers over the communication bandwidth is a key requirement of high performance optical wavelength division multiplexing (WDM) communication systems. Most often, a gain flattening filter (GFF) with a spectral response matching the inverse gain profile is incorporated within the amplifier. The chirped fiber Bragg grating (CFBG) is an attractive technology to produce GFFs, especially in cases where very low error functions are required. Error functions smaller than or equal to ±0.1 dB for the full operating temperature range are now possible. Moreover, the systematic errors from cascaded filters are much smaller than for thin-film GFF, a factor of importance in a long chain of amplifiers. To achieve this performance level, the high-frequency ripples normally associated with CFBG-GFF have been reduced by combining state-of-the-art holographic phase masks and advanced UV-writing techniques. Lastly, to eliminate the residual low-frequency ripples and localized errors, we developed a laser annealing-trimming station. This fully automated station combines both the aging process and final trimming of the GFF refractive index profile to exactly match the required transmission spectra. The use of self-adjusting algorithms assures quick convergence of the error function within a very tight error band. The capital expenditure necessary to implement this new tool is small in relation to the gain in precision, reliability and manufacturing cycle time.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".