A Pseudo pMOS Resistive OTA with Ultra-Low Noise and PVT-Robust Performance for Smart Neuro-Rehabilitation Wearables

Authors

  • Sarin Vijay Mythry
  • Shailaja Mantha
  • Mahesh Koti
  • N Dinesh Kumar
  • Swapna Thouti
  • Katapaka Yadaiah
  • Shashi Kant Gupta
  • Sai Kiran Oruganti

DOI:

https://doi.org/10.51483/IJAIML.6.3.2026.907-929

Keywords:

Operational Transconductance Amplifier (OTA), Common-Source PMOS Topology, EEG Wearable Devices, Low-Noise Amplifier, PVT Variability, Ultra-Low Power Design, Biomedical Signal Processing.

Abstract

In smart wearable devices, a high-performance operational transconductance amplifier (OTA) for portable EEG data collection is designed and analysed for various parameters. With 14.5 nV/Hz input-referred noise, the 130 nm CMOS common source pseudo pMOS resistive OTA architecture can safely amplify weak EEG signals from 0.5 to 100 Hz at 1.8 V With 125 dB CMRR and 77 dB open-loop gain, the amplifier reduces external interference and motion artifacts. 2.2 µW power consumption enhances battery life for continuous monitoring in wearable biomedical devices. Investigation shows 4.59 nVRMS integrated output noise and 2.11 × 10⁻¹⁷ V² overall noise power. OTA's 1–2 continuous NEF and 1.8–7.2 PEF balance noise and power. Consistent 10 V/µs slew rate ensures accurate signal tracking. Supply variations, process corners, and -40°C to +125°C performance are consistent in extensive PVT simulations. The custom OTA design reduces flicker noise and mismatch using symmetry, local shielding, and common-centroid structures. The layout measures 73.99 µm width and 25.1 µm length, covering 1850 µm² of area. The tiny size suits modern wearable EEG front-ends. These results imply the OTA is a compact, noise-resistant, and energy-efficient analog front-end for smart wearables and BCIs.

Downloads

Published

2026-09-24

How to Cite

Mythry, S. V., Mantha, S., Koti, M., Kumar, N. D., Thouti, S., Yadaiah, K., … Oruganti, S. K. (2026). A Pseudo pMOS Resistive OTA with Ultra-Low Noise and PVT-Robust Performance for Smart Neuro-Rehabilitation Wearables . International Journal of Artificial Intelligence and Machine Learning, 6(3), 907–929. https://doi.org/10.51483/IJAIML.6.3.2026.907-929