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Actively Multiplexed μECoG Brain Implant System With Incremental-ΔΣ ADCs Employing Bulk-DACs

Xiaohua Huang, Horacio Londoño‐Ramírez, Marco Ballini, Chris Van Hoof, Jan Genoe, Sebastian Haesler, Georges Gielen, Nick Van Helleputte, Carolina Mora López

2022IEEE Journal of Solid-State Circuits25 citationsDOI

Abstract

Fundamental neuroscience research and high-performance neuro-prostheses require large-scale brain interfaces with ever-greater spatial resolution across a large cortex coverage, which cannot be achieved with current passive (micro) electrocorticography (ECoG) technologies. In this article, we present an active micro-electrocorticography ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> ECoG) implant system that circumvents these challenges while achieving significantly lower noise compared to other existing active <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> ECoG arrays. The proposed brain implant system is composed of a flexible, actively multiplexed 256-electrode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> ECoG array and an incremental- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> readout integrated circuit (ROIC). The 1 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> ECoG array was fabricated in a 3- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> IGZO thin-film transistor (TFT) technology on a 15- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> flexible foil and coupled to a 1.25 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.25 mm CMOS ROIC fabricated in a 22-nm fully depleted silicon on insulator (FDSOI) process. Due to the 256:16 time-division multiplexing achieved in the electrode array, only 16 multiplexed channels are required in the ROIC to acquire signals from the 256 electrodes simultaneously. By combining TFT multiplexing with newly proposed bulk-DAC (BDAC) feedback in the readout channel, we can integrate and address 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> more electrodes than other passive arrays, achieve >10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> less noise than existing active arrays, and obtain >2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> effective channel area reduction in the ROIC while maintaining comparable electrical performance over current state-of-the-art ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> )ECoG readouts.

Topics & Concepts

NotationMathematicsAlgorithmComputer scienceArithmeticNeuroscience and Neural EngineeringAdvanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit Design