Akuri, N.G.Naik, D.N.Kumar, S.Song, H.Kar, A.2026-02-042024Journal of Semiconductor Technology and Science, 2024, 24, 3, pp. 270-28315981657https://doi.org/10.5573/JSTS.2024.24.3.270https://idr.nitk.ac.in/handle/123456789/21065An ultra-low noise, Tera-ohm input impedance two-stage front-end neural amplifier (FENA) in the 28 nm CMOS process is presented in this work. As per the author’s best knowledge, the proposed FENA is implemented on a 28 nm CMOS process for the first time. The proposed FENA consists of an operational transconductance amplifier integrated low-pass filter (LPF) technique. This technique effectively removes the noise current density by using the LPF transfer function and FENA circuit to achieve the best performances, such as ultra-low input-referred noise, ultra-high input impedance, and high gain. The proposed mathematical technique is employed to optimize the dimensions of the neural amplifier in the 28 nm lower node, which results in a noise-free biasing current and ultra-low input referred noise of 18 fV/√Hz at 10 KHz. The ultra-low input referred noise of FENA is achieved by reducing the gate-distributed resistance method. The FENA achieves an ultra-high input impedance of 0.2 Tera-ohm, while a splendid measured gain of 60 dB has succeeded. FENA occupies a chip area of 0.0023 mm2, which consumes a lower power consumption of 1 µW under supply voltage of 1.2 V. The FENA is found to be less prone to PVT variations as 1 mHz of high-pass corner frequency towards robust design. The best performance parameters of FENA could be beneficial for deep exploration neural recording in wireless neural monitoring systems. © 2024, Institute of Electronics Engineers of Korea. All rights reserved.CMOS integrated circuitsElectric impedanceElectric impedance measurementMOS devicesNeurophysiologyOperational amplifiersOxide semiconductorsBio-medicalComplementary metal oxide semiconductorComplementary metal oxide semiconductor processComplementary metal oxide semiconductorsFront endFront-end amplifierLow-pass filtersNeural recordingsNeural systemsUltra low noiseLow pass filters