An Energy-Efficient Pipeline-SAR ADC Using Linearized Dynamic Amplifiers and Input Buffer in 22nm FDSOI
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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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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 | 0.001 | 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 it