A 100-Channel 1-mW Implantable Neural Recording IC
This paper presents a fully implantable 100-channel neural interface IC for neural activity monitoring. It contains 100-channel analog recording front-ends, 10 multiplexing successive approximation register ADCs, digital control modules and power management circuits. A dual sample-and-hold architect...
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Main Authors: | , , , , , , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2016
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/81707 http://hdl.handle.net/10220/39645 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | This paper presents a fully implantable 100-channel neural interface IC for neural activity monitoring. It contains 100-channel analog recording front-ends, 10 multiplexing successive approximation register ADCs, digital control modules and power management circuits. A dual sample-and-hold architecture is proposed, which extends the sampling time of the ADC and reduces the average power per channel by more than 50% compared to the conventional multiplexing neural recording system. A neural amplifier (NA) with current-reuse technique and weak inversion operation is demonstrated, consuming 800 nA under 1-V supply while achieving an input-referred noise of 4.0 μVrms in a 8-kHz bandwidth and a NEF of 1.9 for the whole analog recording chain. The measured frequency response of the analog front-end has a high-pass cutoff frequency from sub-1 Hz to 248 Hz and a low-pass cutoff frequency from 432 Hz to 5.1 kHz, which can be configured to record neural spikes and local field potentials simultaneously or separately. The whole system was fabricated in a 0.18-μm standard CMOS process and operates under 1 V for analog blocks and ADC, and 1.8 V for digital modules. The number of active recording channels is programmable and the digital output data rate changes accordingly, leading to high system power efficiency. The overall 100-channel interface IC consumes 1.16-mW total power, making it the optimum solution for multi-channel neural recording systems. |
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