A Multi-Stage RC Compensation Technique for Decoupling the Transimpedance and BW: Creating High Speed and Low Noise TIA Designs
Bibliographic record
Abstract
The gain and bandwidth of a shunt-feedback Transimpedance Amplifier (TIA) is limited by a so called transimpedance (TI) limit. This limit dictates the maximum possible value of the feedback resistance (RF) for a targeted bandwidth. Additionally, the input referred noise of such TIAs is inversely proportional to theRF, which presents a challenge in simultaneous optimization of bandwidth, noise and transimpedance gain. In this paper, the TI limit is revisited, and a multi-stage RC compensation technique is presented for the design of the open-loop amplifier for a closed-loop shunt-feedback-based TI stage. This paper shows that with the appropriate pole-zero positioning, the DC transimpedance gain can be decoupled from the closed-loop TI bandwidth. This is achieved by placing a zero in the open loop transfer function to reduce the impact of the closed loop dominant pole created by the input capacitance and the RF. As a result, without the need for area consuming inductors, a TI stage is realized which has a transimpedance limit that is larger than the conventionally assumed limit. Additionally, the proposed RC compensation network provides more control over the pole-zero positioning which results in smooth overall frequency response after equalization. This is verified by experimental results which show that the proposed technique achieves a much greater transimpedance gain as compared to that of the conventional limit while reducing the noise and without any significant deterioration of bandwidth. The design has been implemented in a 90 nm BiCMOS process from Global Foundries (GF-9HP). A detailed comparison of the proposed approach is presented with other TIA designs. As per the author’s best knowledge, the proposed design outperforms the state-of-the-art TIA designs in terms of the noise-transimpedance-bandwidth trade-off.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".