Feedback methods for inductorless bandwidth extension and linearisation of post-amplifiers in optical receiver frontends
Bibliographic record
Abstract
Optical communication is increasingly important in today's telecommunications. It is not only a key component in long-haul infrastructure, but is also being brought into new applications within the datacentre, at the circuit board and integrated circuit level, and in next generation mobile networks. This thesis proposes feedback tuning approaches in order to address two challenges within optical receiver analog frontend circuits: a) the dynamic response of a prior bandwidth extension technique; and b) linearity optimisation. \n \nTo address dynamic response, we begin with an inductorless method of bandwidth extension using positive feedback loops. In a multi-stage post-amplifier with local positive feedback loops, we propose an approach which tunes each positive feedback gain separately, and demonstrate that this achieves better dynamic response and eye opening than the prior equal-feedback-gain approach. We additionally propose root-locus analysis as a means of characterising dynamic response, and suggest some design guidelines based on this analysis. \n \nTo address linearity optimisation, we propose the use of an interleaving negative-feedback post-amplifier topology, previously proposed only for bandwidth extension. We investigate the relationship between the feedback gains and linearity and develop a design approach for linearity optimisation. We then designed and fabricated two 70 dB 6 GHz optical receiver circuits, making use of two different post-amplifiers, in order to compare different design approaches. We achieved a linearity of 0.08 dBVrms OIP3 (quasi-static) and a THD of 0.195\\% at 1 GHz.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".