An Inductorless Optical Receiver Front-End Employing a High Gain-BW Product Differential Transimpedance Amplifier in 16-nm FinFET Process
Bibliographic record
Abstract
In this paper, a fully-differential transimpedance amplifier (TIA) providing a high gain-BW product (GBP) is introduced. In the proposed architecture, a cascode cross-coupled structure is employed to double the effective transconductance of the cascode devices, improving the BW of the TIA. Moreover, a differential architecture is implemented using an RC high-pass filter along with a buffer stage requiring smaller capacitance and resistance. Furthermore, a single-ended negative capacitance generation (NCG) circuit is employed at the input of the TIA to partially compensate for the input parasitic capacitances. A TIA including the proposed techniques, designed and laid out in a 16-nm FinFET process, demonstrates 57% and 79% better figure-of-merit compared to cascode and conventional TIAs designed along with the proposed TIA for a fair comparison, respectively. Post-layout simulations in companion with statistical analysis are employed to verify the effectiveness of the proposed architecture. From simulation results, the optical receiver achieves a peak transimpedance gain of 58.5 dBΩ, a BW of 14.8 GHz, an input-referred noise of 33.6 pA/Hz, and an eye-opening of 30 mV at a data-rate of 56 Gbps PAM4 and at a bit-error-rate (BER) of 1E-6. The whole circuit consume 49 mW and occupies an active area of 0.0076 mm2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".