Stable and Reduced-Linewidth Laser Through Active Cancellation of Reflections Without a Magneto-Optic Isolator
Bibliographic record
Abstract
Integrating photonics with CMOS electronics in silicon is essential to enable chip-scale, electronic-photonic systems that will revolutionize classical and quantum communication and computing systems. However, the lack of an on-silicon isolator, capable of blocking unwanted back reflections and ensuring the stable operation of the laser, precluded many previous demonstrations from providing single-chip solutions. For most optical systems employing a laser, magneto-optic isolators have been indispensable, but such isolators are incompatible with silicon. To stabilize on-chip lasers, reflections-cancellation circuits were proposed as a way to reduce the reflections going back to the laser. Yet, a stable laser against time-varying back reflections was never demonstrated. Here we demonstrate a stable quantum well-distributed feedback (QWDFB) laser against slowly time-varying reflections using a reflections-cancellation circuit (RCC) on a foundry-produced, silicon-photonic (SiP) chip. The optical spectrum and the relative intensity noise (RIN) of the laser when the RCC was running is comparable to when an isolator was used. By accurately locking the laser in a stable optical feedback regime, the RCC further enhances the QWDFB laser performance by reducing its linewidth by a factor of 100, down to 3 kHz. Both results are enabled using novel techniques in the design, calibration, tuning, and control of the proposed SiP RCC. The optical insertion loss of the RCC is less than 1.5 dB for reflections smaller than −20 dB and can yield isolation ranges of up to 64 dB. Our device paves the way towards the mass production of fully integrated, low-cost electronic-photonic silicon chips without attaching magneto-optic isolators between the laser and the SiP chip.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".