A Low-Noise High-Gain Broadband Transformer-Based Inverter-Based Transimpedance Amplifier
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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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> , 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 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it