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 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>F</i></sub>) for a targeted bandwidth. Additionally, the input referred noise of such TIAs is inversely proportional to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>F</i></sub>, 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 R<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><roman>F</roman></sub>. 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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".