A 0.6V 1.07 μW/Channel neural interface IC using level-shifted feedback

Liangjian Lyu; Yu Wang; Chixiao Chen; C. J. Richard Shi

doi:10.1016/j.vlsi.2019.11.001

A 0.6V 1.07 μW/Channel neural interface IC using level-shifted feedback

Liangjian Lyu^*
, Yu Wang
, Chixiao Chen
, C. J. Richard Shi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

This paper presents a 16-channel low-power neural interface IC for in-vivo neural recording applications. Each recording channel consists of two AC-coupled close-loop amplifiers: a low noise amplifier (LNA) amplifies the weak neural signal by 26 dB, and a programmable gain amplifier (PGA) provides an additional gain of 8 dB–26 dB. The LNA employs a current-reusing telescopic topology to reduce noise for achieving a better noise efficiency factor (NEF). To further reduce the power consumption, we propose a level-shifted feedback (LSFB) technique to lower the operational supply voltage. Theoretic analysis shows that the proposed amplifier with LSFB halves the minimum required supply voltage compared to conventional AC-coupled close-loop counterparts without degenerating the noise performance. The prototype chip is fabricated in 65 nm CMOS process. Operating under a single 0.6 V supply, each recording channel consumes 1.07 μW. The input-referred noise integrating from 10 Hz to 10 kHz is 5.18 μV, and the NEF/PEF is 2.94/5.19.

Original language	English
Pages (from-to)	51-59
Number of pages	9
Journal	Integration
Volume	70
DOIs	https://doi.org/10.1016/j.vlsi.2019.11.001
State	Published - Jan 2020
Externally published	Yes

Keywords

Current-reusing
Level-shifted feedback
Low noise amplifier
Low voltage
Neural interface

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10.1016/j.vlsi.2019.11.001

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@article{7b9f71c5323d4a35ae461447357b21e3,

title = "A 0.6V 1.07 μW/Channel neural interface IC using level-shifted feedback",

abstract = "This paper presents a 16-channel low-power neural interface IC for in-vivo neural recording applications. Each recording channel consists of two AC-coupled close-loop amplifiers: a low noise amplifier (LNA) amplifies the weak neural signal by 26 dB, and a programmable gain amplifier (PGA) provides an additional gain of 8 dB–26 dB. The LNA employs a current-reusing telescopic topology to reduce noise for achieving a better noise efficiency factor (NEF). To further reduce the power consumption, we propose a level-shifted feedback (LSFB) technique to lower the operational supply voltage. Theoretic analysis shows that the proposed amplifier with LSFB halves the minimum required supply voltage compared to conventional AC-coupled close-loop counterparts without degenerating the noise performance. The prototype chip is fabricated in 65 nm CMOS process. Operating under a single 0.6 V supply, each recording channel consumes 1.07 μW. The input-referred noise integrating from 10 Hz to 10 kHz is 5.18 μV, and the NEF/PEF is 2.94/5.19.",

keywords = "Current-reusing, Level-shifted feedback, Low noise amplifier, Low voltage, Neural interface",

author = "Liangjian Lyu and Yu Wang and Chixiao Chen and \{Richard Shi\}, \{C. J.\}",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

year = "2020",

month = jan,

doi = "10.1016/j.vlsi.2019.11.001",

language = "英语",

volume = "70",

pages = "51--59",

journal = "Integration",

issn = "0167-9260",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - A 0.6V 1.07 μW/Channel neural interface IC using level-shifted feedback

AU - Lyu, Liangjian

AU - Wang, Yu

AU - Chen, Chixiao

AU - Richard Shi, C. J.

PY - 2020/1

Y1 - 2020/1

N2 - This paper presents a 16-channel low-power neural interface IC for in-vivo neural recording applications. Each recording channel consists of two AC-coupled close-loop amplifiers: a low noise amplifier (LNA) amplifies the weak neural signal by 26 dB, and a programmable gain amplifier (PGA) provides an additional gain of 8 dB–26 dB. The LNA employs a current-reusing telescopic topology to reduce noise for achieving a better noise efficiency factor (NEF). To further reduce the power consumption, we propose a level-shifted feedback (LSFB) technique to lower the operational supply voltage. Theoretic analysis shows that the proposed amplifier with LSFB halves the minimum required supply voltage compared to conventional AC-coupled close-loop counterparts without degenerating the noise performance. The prototype chip is fabricated in 65 nm CMOS process. Operating under a single 0.6 V supply, each recording channel consumes 1.07 μW. The input-referred noise integrating from 10 Hz to 10 kHz is 5.18 μV, and the NEF/PEF is 2.94/5.19.

AB - This paper presents a 16-channel low-power neural interface IC for in-vivo neural recording applications. Each recording channel consists of two AC-coupled close-loop amplifiers: a low noise amplifier (LNA) amplifies the weak neural signal by 26 dB, and a programmable gain amplifier (PGA) provides an additional gain of 8 dB–26 dB. The LNA employs a current-reusing telescopic topology to reduce noise for achieving a better noise efficiency factor (NEF). To further reduce the power consumption, we propose a level-shifted feedback (LSFB) technique to lower the operational supply voltage. Theoretic analysis shows that the proposed amplifier with LSFB halves the minimum required supply voltage compared to conventional AC-coupled close-loop counterparts without degenerating the noise performance. The prototype chip is fabricated in 65 nm CMOS process. Operating under a single 0.6 V supply, each recording channel consumes 1.07 μW. The input-referred noise integrating from 10 Hz to 10 kHz is 5.18 μV, and the NEF/PEF is 2.94/5.19.

KW - Current-reusing

KW - Level-shifted feedback

KW - Low noise amplifier

KW - Low voltage

KW - Neural interface

UR - https://www.scopus.com/pages/publications/85074870340

U2 - 10.1016/j.vlsi.2019.11.001

DO - 10.1016/j.vlsi.2019.11.001

M3 - 文章

AN - SCOPUS:85074870340

SN - 0167-9260

VL - 70

SP - 51

EP - 59

JO - Integration

JF - Integration

ER -

A 0.6V 1.07 μW/Channel neural interface IC using level-shifted feedback

Abstract

Keywords

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