A Reconfigurable Current-Mode Antialiasing Filter for Low-Frequency Industrial Applications
Bibliographic record
Abstract
This paper presents a current-mode antialiasing filter (AAF) based on second-generation current-controlled current conveyors (CCCII). It is specifically aimed at kHz-range industrial applications. The filter comprises two CCCIIs and two triple-MIM (Metal-Insulator-Metal) grounded capacitors. An OpAmp-based voltage-controlled current source handles the bias current control. The cutoff frequency of the proposed second-order AAF can be continuously tuned by exploiting the inversely proportional relationship between the parasitic impedance at the input X-terminal of the CCCII and its DC bias current, without relying on any additional circuitry. The performance of the proposed design has been validated through post-layout and corner SPECTRE simulations using a 0.18$\mu \mathrm{m}$HV CMOS SOI process. The$-3\ \mathrm{d}\mathrm{P}$cutoff frequency can be continuously tuned between 3.9 kHz and 34.9 kHz. A passband gain error of 1.127 % is reported, along with a Total Harmonic Distortion (THD) of 3.96 % for a 20$\mu \mathrm{A}$peak-to-peak, 5 kHz sine wave input. The solution requires 0.67$mm^{2}$core area when leveraging triple-Mimcapacitors offered in the process. The proposed architecture achieves a control accuracy of 0.32 % with respect to the associated theoretical model, and 0.951 decades of tuning range. It consumes 3.52 mW under a supply voltage of 5 V.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".