A Microscale Neural Implant ASIC for Scalable Multichannel Brain-Machine Interface
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The Brain-Machine Interface (BMI) is a pivotal tool for the study and repair of the nervous system, facilitating connections between the brain and external devices. The implementation of a flexible and adaptable multichannel system is essential for effective communication and analysis of a large number of neurons in the anatomically and physiologically complex human nervous system. This paper presents the development of a wireless stimulation and recording ASIC, fundamental for creating a modular BMI system with multiple modules. Simulations reveal the on-chip antenna's peak efficiency at the target frequency within a streamlined design. The inclusive wireless power system, featuring the resonant tank, rectifier, and LDOs, delivers a 1.2 V supply voltage, allocating 22.56 μW to the recording subsystem, while providing 9.7 μW and 2.1 mW to resting and active stimulators, respectively. The spike detection front-end, utilizing an energy operator, conveys data via backscatter communication, and the stimulation back-end executes 8-channel biphasic current stimulation, marking a notable stride in the evolution of flexible and adaptive BMI technology.
Abstract
The Brain-Machine Interface (BMI) is a pivotal tool for the study and repair of the nervous system, facilitating connections between the brain and external devices. The implementation of a flexible and adaptable multichannel system is essential for effective communication and analysis of a large number of neurons in the anatomically and physiologically complex human nervous system. This paper presents the development of a wireless stimulation and recording ASIC, fundamental for creating a modular BMI system with multiple modules. Simulations reveal the on-chip antenna's peak efficiency at the target frequency within a streamlined design. The inclusive wireless power system, featuring the resonant tank, rectifier, and LDOs, delivers a 1.2 V supply voltage, allocating 22.56 μW to the recording subsystem, while providing 9.7 μW and 2.1 mW to resting and active stimulators, respectively. The spike detection front-end, utilizing an energy operator, conveys data via backscatter communication, and the stimulation back-end executes 8-channel biphasic current stimulation, marking a notable stride in the evolution of flexible and adaptive BMI technology.
참고문헌 (16)
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