A Low-Complexity Delta-Sigma Modulator (<inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math> </inline-formula>) for Low-Voltage, Low-Power Operation
Bibliographic record
Abstract
In recent years, inverter-based sigma-delta (ΔΣ) modulators have received great attention as a suitable approach for the design of low-voltage, low-power, switched-capacitor ΔΣ. This method uses digital inverters as the active elements to construct the integrators in the ΔΣ loop. In some applications, a reduced silicon area implementation is an important constraint; this demands the use of only one inverter in the integrator. This paper proposes to use the common-source amplifier as the building block to achieve the operation of integration instead of the digital inverter. This leads to the operation of the amplifier in strong inversion combined with low-voltage supply and compact chip area. The idea was confirmed with a prototype fabricated in a 0.5-μm CMOS technology available through Metal Oxide Semiconductor Implementation Service (MOSIS). Using 250 kHz of sampling frequency, measurement results show a signal-to-noise and distortion ratio of 70 dB over a bandwidth of 125 Hz. The circuit consumes 38 μW when powered from a single 1.5-V supply voltage and uses 200 × 260 μm of active area. Circuit simulations in SPICE show the potential of this method to work with 450 mV of power supply in a 50-nm CMOS technology.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".