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

被引:0
|
作者
Akuri, Naga Ganesh [1 ]
Jatoth, Deepak Naik [3 ]
Kumar, Sandeep [1 ,2 ]
Song, Hanjung [3 ]
Kar, Asutosh [4 ]
机构
[1] Natl Inst Technol Karnataka, Dept E&C Engn, Surathkal, Mangaluru, India
[2] Jawaharlal Nehru Univ JNU, Special Ctr Nanosci, New Delhi, India
[3] Inje Univ, Ctr Nano Mfg, Dept NanoSci & Engn, Gimhae 621749, South Korea
[4] Natl Inst Technol Jalandhar, Dept Elect & Commun Engn, Jalandhar, Punjab, India
基金
新加坡国家研究基金会;
关键词
Front-end amplifier; low-pass filter; complementary metal oxide semiconductor (CMOS); bio-medical; neural system; INPUT IMPEDANCE; FEEDBACK; ARRAY;
D O I
10.5573/JSTS.2024.24.3.270
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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, 2 , which consumes a lower power consumption of 1 mu W 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.
引用
收藏
页码:270 / 283
页数:14
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