DESIGN OF VCO-BASED CONTINUOUS-TIME ADCS FOR NEURAL RECORDING APPLICATIONS

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SP23-ECE499-Thesis-Nathan, Prithvi.pdf

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https://hdl.handle.net/2142/124812

Title: DESIGN OF VCO-BASED CONTINUOUS-TIME ADCS FOR NEURAL RECORDING APPLICATIONS
Author(s): Nathan, Prithvi
Issue Date: 2023-05-01
Keyword(s): brain-machine interfaces, neural recording circuits, analog-to-digital conversion circuits
Date of Ingest: 2024-10-04T16:20:37-05:00
Abstract: Analog-to-digital converters for neural recording circuits require significant signal integrity constraints to measure action potentials. As technology for electrophysiology and brain-machine interfaces becomes more advanced, ad vances in analog-to-digital conversion circuits are needed. One particularly effective strategy for high-SNR ADCs is delta-sigma modulation to correct for errors induced by quantization noise. In this thesis, the fundamental theory and design constraints of delta-sigma modulation in neural-recording circuits will be explored as well as the implementation of a VCO-based quantizer to significantly reduce area and power constraints while maintaining high SNR. Preliminary simulations of a first-order loop filter followed by a VCO-based quantizer can achieve 92.9 dB of signal-to-quantization noise ratio (SQNR) at an oversampling ratio (OSR) of 500.
Type of Resource: text
Genre of Resource: dissertation/thesis
Language: eng

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