Compact silicon photonics-based laser modules for FM-CW LIDAR and RFOG
Bibliographic record
Abstract
Narrow-linewidth semiconductor lasers, micro-optics, silicon photonics (SiP), low noise electronics and high-density packaging are key elements for the development of compact high-end light sources for sensing. A laser module for the interrogation of an RFOG (Resonant Fiber-Optic Gyroscope) includes three distributed feedback lasers coupled with micro-lenses to a multi-component SiP chip that performs beat note detection and several other functions. The lasers and SiP chip are packaged in a 2.6 cm3 multi-layer ceramic package, a 4x volume reduction over a first generation module. The package interfaces with 92 electrical pins and two fiber pigtails, one carrying the signals from a master and slave lasers, another carrying that from a second slave laser. The complete laser source including electronics is 60 mm in diameter and 23 mm in height, a 10x volume improvement over a previous version. The master laser can be locked to the RFOG resonator with a loop bandwidth greater than 1 MHz. The slave lasers are offset frequency locked to the master laser with loop bandwidths greater than 100 MHz. This high performance source is compact, automated, robust, and remains locked for days. A lighter version of this laser module for FM-CW LIDAR applications produces an output optical frequency that varies linearly as a function of the electrical drive. A triangular modulation at 100 kHz with a greater than 1 GHz amplitude has been demonstrated with a linearity noise near 1 MHz as measured through a 150 m unbalanced interferometer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".