Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm2 and an Ultra-High Input Impedance
Bibliographic record
Abstract
Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.
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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 | 0.000 |
| 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) | 0.000 | 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".