A Low-Noise High-Gain Broadband Transformer-Based Inverter-Based Transimpedance Amplifier
Bibliographic record
Abstract
In this paper, a transformer-based bandwidth (BW) extension technique is employed to improve the BW, noise, and silicon area of inverter-based transimpedance amplifiers (TIAs) even when they use inductive peaking. A TIA based on the proposed technique, designed and laid out in a 16-nm FinFET process, demonstrates a 36% increased in BW, a 19% reduction in input-referred noise, and a 57% reduction in silicon area compared to the conventional TIA with inductive peaking. In the proposed TIA architecture, inclusion of a transformer in the forward path compensates partially for the parasitic capacitances of the inverter and relaxes the transimpedance limit of the conventional TIA. The proposed technique also lowers the input-referred current noise spectrum of the TIA. Post-layout in companion with electromagnetic (EM) simulations and statistical analysis are employed to verify the effectiveness of the proposed architecture. Simulation results show that the TIA achieves a transimpedance gain of 58 dB$\Omega $, a BW of 17.4 GHz, an input-referred noise of 17.4 pA/sqrt (Hz), and an eye-opening of 20 mV at a data-rate of 64 Gbps PAM4 and at a bit-error-rate (BER) of 1E-6. The whole TIA chain is expected to consume 19 mW and occupies an active area of 0.023 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".