A 100-Channel 1-mW Implantable Neural Recording IC

• المؤلفون الرئيسيون: Zou, Xiaodan, Liu, Lei, Cheong, Jia Hao, Yao, Lei, Li, Peng, Cheng, Ming-Yuan, Goh, Wang Ling, Rajkumar, Ramamoorthy, Dawe, Gavin Stewart, Cheng, Kuang-Wei, Je, Minkyu
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2016
• الموضوعات: Capacitor-less LDO; High power efficiency; Multi-channel neural recording system; Power and area trade-off; Dual S/H; NEF; Current reuse; SAR ADC; Biomedical application; Low-noise neural amplifier
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/81707; http://hdl.handle.net/10220/39645

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.