Analysis of Dynamic Element Matching (DEM) in Pipelined ADCs
Bibliographic record
Abstract
Dynamic element matching (DEM), typically used to enhance the linearity of the digital-to-analog converter (DAC) in a multi-bit DeltaSigma modulator, can also be utilized to linearize the sub-DAC in a multi-bit pipeline stage of a pipelined analog-to-digital converter (ADC). In this paper, an analytical approach to estimate the effect of DEM on the spurious-free dynamic range (SFDR) of a pipelined ADC is presented. A closed-form expression for the 3rd-harmonic power and, hence, the SFDR of a pipelined ADC is derived. The proposed analysis shows that performing DEM in the first stage of a pipelined ADC reduces the 3rd-harmonic power in the ADC output spectrum by a factor of (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -1), where m is the number of effective bits in the first pipeline stage. Behavioral Monte-Carlo simulations are presented to demonstrate the accuracy of the proposed analysis
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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 | 0.001 |
| 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".