Low-noise Amplifier for Neural Recording
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Date
2015-10-30
Authors
Srivastava, Rachna
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
With a combination of engineering approaches and neurophysiological knowledge of
the central nervous system, a new generation of medical devices is being developed to link groups of neurons with microelectronic systems. By doing this, researchers are acquiring fundamental knowledge of the mechanisms of disease and innovating treatments for disabilities in patients who have a failure of communication along neural pathways.
A low-noise and low-power analog front-end circuit is one of the primary requirements
for neural recording. The main function for the front-end amplifier is to provide gain over
the bandwidth of neural signals and to reject undesired frequency components. The chip
developed in this thesis is a field-programmable analog front-end amplifier consisting of
16 programmable channels with tunable frequency response. A capacitively coupled two-stage amplifier is used. The first-stage amplifier is a Low-Noise Amplifier (LNA), as it
directly interfaces with the neural recording micro-electrodes; the second stage is a high
gain and high swing amplifier. A MOS resistor in the feedback path is used to get tunable
low-cut-off frequency and reject the dc offset voltage.
Our design builds upon previous recording chips designed by two former graduate stu-
dents in our lab. In our design, the circuits are optimized for low noise. Our simulations
show the recording channel has a gain of 77.9 dB and input-referred noise of 6.95 µV rms(Root-Mean-Square voltage) over 750 Hz to 6.9 kHz. The chip is fabricated in AMS 0.35 µm CMOS technology for a total die area of 3 x 3 mm 2 and Total Power Dissipation (TPD) of 2.9 mW. To verify the functionality and adherence to the design specifications it will be tested on Printed-Circuit-Board.
Description
Keywords
Neural Recording, Low-noise Amplifier, High-gain Amplifier, Neural Disabilities, Field-programmable analog front-end