An Energy-Efficient Pipeline-SAR ADC Using Linearized Dynamic Amplifiers and Input Buffer in 22nm FDSOI
Bibliographic record
Abstract
Recently, dynamic amplifier (DA) has emerged as a popular alternative to static current closed-loop operational transconductance amplifier (OTA) due to their highly power-efficient integration-based settling, with the main limitation being their linearity performance. We present a DA that achieves −52 dB in total harmonic distortion (THD) through an analog technique by which the expanding and compressing nonlinearities in the input transistors cancel one another. A pipeline-SAR analog-to-digital converter (ADC) incorporating the linearized DA in both the input buffer and the first residue amplifier (RA) stage was designed and fabricated using the GlobalFoundries 22nm fully depleted silicon-on-insulator (FDSOI) process. Measurements showed the ADC achieved a signal-to-noise-distortion ratio (SNDR) of 37 dB at 920 MS/s consuming a total power of 1.8mW for a Walden FOM (FOMW) of 34.9 fJ/conv. With the input buffer, the achieved FOMW is 68.4 fJ/conv. The linearization technique provided a 8 dB improvement in SNDR at its optimal biasing with a negligible power overhead of approximately 5%. In general, it is expected that an 8 dB SNDR improvement would require 2.5 times the power consumption for a mismatch-limited design (Walden FOM) or 6.3 times the power for a noise-limited design (Schreier FOM).
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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) | 0.002 | 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".