An Ultra-low Noise, Highly Compact Implantable 28 nm CMOS Neural Recording Amplifier
No Thumbnail Available
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electronics Engineers of Korea
Abstract
An 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.
Description
Keywords
CMOS integrated circuits, Electric impedance, Electric impedance measurement, MOS devices, Neurophysiology, Operational amplifiers, Oxide semiconductors, Bio-medical, Complementary metal oxide semiconductor, Complementary metal oxide semiconductor process, Complementary metal oxide semiconductors, Front end, Front-end amplifier, Low-pass filters, Neural recordings, Neural systems, Ultra low noise, Low pass filters
Citation
Journal of Semiconductor Technology and Science, 2024, 24, 3, pp. 270-283
