Switch-Less Frequency-Domain Multiplexing of GFET Sensors and Low-Power CMOS Frontend for 1024-Channel μECoG

Cisneros-Fernández, José; Dei, Michele; Terés, Lluís; Serra-Graells, F.

doi:10.1109/iscas.2019.8702544

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…In addition, we have evaluated the crosstalk among sensing sites, which could reach the same level as for TDM with on-site switches when translating this technology to human scale neural probes. In order to maintain the sensitivity of the system for large arrays with up to 32 superposed carriers, we have described the use of carrier phase optimization as well as the requirements for the DAQ system, which could be met by an ASIC …”

Section: Discussionmentioning

confidence: 99%

“…In order to maintain the sensitivity of the system for large arrays with up to 32 superposed carriers, we have described the use of carrier phase optimization as well as the requirements for the DAQ system, which could be met by an ASIC. 35 Nano Letters The viability of large arrays controlled by an ASIC allows one to envision the realization of a new generation of highdensity and large-area sensor arrays. The simplicity and robustness of the switchless, FDM methodology compared to state-of-the-art alternatives, 33,39 together with the high sensitivity, flexibility and biocompatibility of graphene active sensors make the implementation of these technologies very promising for both neuroscientific research as well as clinical applications.…”

Section: ■ Discussionmentioning

confidence: 99%

“…The comparison with the superposition of the 4 carriers used for this proof of concept shows that upscaling to 32 carriers causes an increase in the amplitude by a factor of 2.8, which could be compensated by increasing the resolution of the ADC by 1− 2 bits. 35 These results, together with the evaluation of crosstalk and the high frequency response of g-SGFETs, demonstrate the potential of this technology for high-count flexible neural probes.…”

Section: Probesmentioning

confidence: 99%

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Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping

et al. 2020

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Sensor arrays used to detect electrophysiological signals from the brain are paramount in neuroscience. However, the number of sensors that can be interfaced with macroscopic data acquisition systems currently limits their bandwidth. This bottleneck originates in the fact that, typically, sensors are addressed individually, requiring a connection for each of them. Herein, we present the concept of frequency-division multiplexing (FDM) of neural signals by graphene sensors. We demonstrate the high performance of graphene transistors as mixers to perform amplitude modulation (AM) of neural signals in situ, which is used to transmit multiple signals through a shared metal line. This technology eliminates the need for switches, remarkably simplifying the technical complexity of state-of-the-art multiplexed neural probes. Besides, the scalability of FDM graphene neural probes has been thoroughly evaluated and their sensitivity demonstrated in vivo. Using this technology, we envision a new generation of high-count conformal neural probes for high bandwidth brain machine interfaces.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Discussionmentioning

confidence: 99%

Section: Probesmentioning

confidence: 99%

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Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping

et al. 2020

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“…It is observed that the GFET device is more acceptable for the narrow range of drain voltage and drain current compared to Si-MOSFET. Cisneros-Fernández et al ( 2019 ) proposed frequency domain multiplexing of liquid-gate GFET sensor for micro electrocorticogram (ECoG) recording purpose. The proposed work also allows hybrid integration.…”

Section: Cmos Compatibilitymentioning

confidence: 99%

Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

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Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

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Switch-Less Frequency-Domain Multiplexing of GFET Sensors and Low-Power CMOS Frontend for 1024-Channel μECoG

Cited by 2 publications

References 23 publications

Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping

Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping

Graphene-based RRAM devices for neural computing

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