A 0.9 V, Ultra-Low-Power OTA with Low NEF and High CMRR for Batteryless Biomedical Front-Ends
Bibliographic record
Abstract
This paper presents a new operational transconductance amplifier (OTA) design for batteryless biomedical front-ends. The proposed OTA operates in the subthreshold region and utilizes self-cascode devices to achieve ultra-low power, low noise, and a high common-mode rejection ratio (CMRR). Post-layout simulations in Cadence, using 45 nm CMOS technology with 0.9 V supply voltage, show a power consumption of 49.3 nW, a CMRR of 144.9 dB, an input-referred noise of 4.51 μVrms integrated over 0.5–208 Hz, and a noise efficiency factor of 1.023 with a core silicon area of 0.00138 mm2. Using the proposed OTA, we implemented a 10-channel neural recording amplifier for Local Field Potentials (LFPs) based on a capacitively coupled, capacitive-feedback (CC-CF) topology with a body-driven pseudo-resistor high-pass path. The system achieves a total CMRR ≥ 70 dB and an estimated power of 494.2 nW for 10 channels. Compared with prior art, the proposed OTA offers competitive noise efficiency and common-mode rejection at lower power, making it a viable building block for batteryless neural and biomedical sensing front-ends.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".