Year: 2015
Type: dissertation
Abstract: With a combination of engineering approaches and neurophysiological knowledge of \nthe 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. \n \nA low-noise and low-power analog front-end circuit is one of the primary requirements \nfor neural recording. The main function for the front-end amplifier is to provide gain over \nthe bandwidth of neural signals and to reject undesired frequency components. The chip \ndeveloped in this thesis is a field-programmable analog front-end amplifier consisting of \n16 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 \ndirectly interfaces with the neural recording micro-electrodes; the second stage is a high \ngain and high swing amplifier. A MOS resistor in the feedback path is used to get tunable \nlow-cut-off frequency and reject the dc offset voltage. \n \nOur design builds upon previous recording chips designed by two former graduate stu- \ndents in our lab. In our design, the circuits are optimized for low noise. Our simulations \nshow 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.
Source: UWSpace (University of Waterloo)
Author: Rachna Srivastava
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2154106718