Design and Analysis of C-Band Tunable On-Chip Integrated CW-MOPA Based on Er–Yb:Co-Doped Waveguides in Lithium Niobate on Insulator Platform
Bibliographic record
Abstract
Erbium–Ytterbium:co-doped Lithium Niobate on insulator (Er–Yb:LNOI) is a promising platform for implementation of photonic integrated circuits (PICs), offering optical gain to the LNOI system, and enabling on-chip lasers and amplifiers. However, a key challenge for Er–Yb:LNOI lasers and amplifiers lies in achieving higher output power and efficiency while preserving single-longitudinal mode operation. We propose for the first time, to the best of our knowledge, an on-chip integrated full C-band tunable continuous wave-master oscillator power amplifier (CW-MOPA) based on Er–Yb:co-doped waveguides (EYCDWs) in a single Lithium Niobate on insulator platform. The EYCDWs in master oscillator (MO) and power amplifier (PA) stages are pumped using 980 nm laser diodes in forward and backward configurations, respectively. The performance of the proposed tunable on-chip integrated CW-MOPA is analyzed over full C-band (1530–1565 nm). Record high on-chip power of 500 mW is achieved at 1545 nm for 50% of the coupling ratio. Besides, highest slope efficiency (SE) and side-mode suppression ratio (SMSR) of 67.4% and 70.7 dB are achieved at 1545 nm for 50% of coupling ratio, respectively. Similarly, minimum linewidth (LW) of 124 MHz is observed for lasing wavelength of 1555 nm considering 60% of the coupling ratio. Finally, the effect of energy transfer upconversion (UC) on the performance of on-chip CW-MOPA is also observed. A power penalty of approximately 140 mW has been observed in on-chip laser power. The demonstrated high-efficiency, tunable CW-MOPA system enables compact and scalable photonic solutions for next-generation applications like 6 G networks, underwater optical communications, LiDAR, and resilient free-space optical links, offering robust performance in challenging environments while outperforming bulk optics in size, power efficiency, and operational reliability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".