An Ultra-low Noise, Highly Compact Implantable 28 nm CMOS Neural Recording Amplifier

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...

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Bibliographic Details
Published inJournal of semiconductor technology and science Vol. 24; no. 3; pp. 270 - 283
Main Authors Akuri, Naga-Ganesh, Jatoth, Deepak-Naik, Kumar, Sandeep, Song, Hanjung, Kar, Asutosh
Format Journal Article
LanguageEnglish
Published 대한전자공학회 01.06.2024
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Summary: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 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. KCI Citation Count: 0
ISSN:1598-1657
2233-4866
2233-4866
1598-1657
DOI:10.5573/JSTS.2024.24.3.270