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Author:

Chen, Weijian (Chen, Weijian.) | Liu, Xu (Liu, Xu.) | Liang, Weisong (Liang, Weisong.) | Lu, Zeyu (Lu, Zeyu.) | Wan, Peiyuan (Wan, Peiyuan.) | Chen, Zhijie (Chen, Zhijie.)

Indexed by:

CPCI-S Scopus

Abstract:

This paper presents a low-power and low-noise front-end amplifier (FEA) dedicated to recording and preprocessing biomedical signals for brain-machine interface. The FEA employs a current-reused architecture which adopts an inverter-based differential input stage to achieve considerable g(m)/I efficiency and low noise. With a carefully designed common-mode feedback circuit, the output common-mode voltage of the fully-differential FEA is stabilized within an acceptable margin of error about 1 mV. All transistors in FEA operate in the sub-threshold region, realizing low power consumption. This current-reused FEA implemented in a CMOS 0.18-mu m technology provides a noise efficiency factor (NEF) and power efficiency factor (PEF) of 1.6 and 2.56, respectively, corresponding to an input-referred noise of 2.37 mu V-rms. This FEA consumes only 2 mu A current from 1 V supply and the active area is 0.2 mm x0.2 mm.

Keyword:

neural recording current-reused low noise brain-machine interface

Author Community:

  • [ 1 ] [Chen, Weijian]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 2 ] [Liu, Xu]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 3 ] [Liang, Weisong]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 4 ] [Lu, Zeyu]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 5 ] [Wan, Peiyuan]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 6 ] [Chen, Zhijie]Beijing Univ Technol, Coll Microelect, Fac Informat, Beijing, Peoples R China
  • [ 7 ] [Liang, Weisong]Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA USA

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Source :

2022 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS, APCCAS

Year: 2022

Page: 144-148

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

Affiliated Colleges:

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