Performance Evaluation of Chopping and Auto-Zeroing based Time-Division Multiplexed High-Density Analog Front-End for Neural Recording

A wide range of neural recording analog front-end (AFE) architectures in the context of closed-loop neuro-modulation SoCs have been presented in the literature. A large majority of the reported time-division multiplexed (TDM) neural AFE operate with the chopping technique to break the trade-off asso...

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Bibliographic Details
Published inBiomedical Circuits and Systems Conference pp. 1 - 5
Main Authors Bal, Arpit, Shrivastava, Aayush, Somappa, Laxmeesha
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.10.2024
Subjects
Analog Front End
Auto-Zeroing
Chopping
Circuits and systems
Impedance
Merging
Neuromodulation
Noise
Performance evaluation
Power demand
Precision Amplifier
Recording
Switch Matrix
Time division multiplexing
Voltage
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ISSN2766-4465
DOI10.1109/BioCAS61083.2024.10798379

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Summary:A wide range of neural recording analog front-end (AFE) architectures in the context of closed-loop neuro-modulation SoCs have been presented in the literature. A large majority of the reported time-division multiplexed (TDM) neural AFE operate with the chopping technique to break the trade-off associated with area, offset, noise and power. While only one architecture proposes a non-conventional auto-zero (AZ) based AFE, the proposed work aims to provide a comparative performance evaluation of chopping and auto-zeroing architecture. The comparison is provided in the context of merging the analog multiplexer with the auto-zero and chopping switches for an efficient TDM-based high-density AFE. Post-layout results of the two 32 -channel designs implemented in a 65 nm CMOS process at 1.2 V supply voltage show that the chopper approach has the best noise performance while suffering from reduced input impedance. Post-layout performance metrics, along with design choices for a fair comparison of the two architectures, are detailed in this work.
ISSN:2766-4465
DOI:10.1109/BioCAS61083.2024.10798379