A neural recording amplifier based on adaptive SNR optimization technique for long-term implantation
| Autor: | Doojin Jang, Sungmin Han, Minkyu Je, Jun-Uk Chu, Soonyoung Hong, Hyuntak Jeon, Junghyup Lee, Taeju Lee, Yoontae Jung |
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| Rok vydání: | 2017 |
| Předmět: |
Physics
Amplifier 020208 electrical & electronic engineering Transistor 02 engineering and technology Chip Noise (electronics) Cutoff frequency law.invention 03 medical and health sciences 0302 clinical medicine Signal-to-noise ratio (imaging) law Control system 0202 electrical engineering electronic engineering information engineering Electronic engineering Electrical impedance 030217 neurology & neurosurgery |
| Zdroj: | BioCAS |
| DOI: | 10.1109/biocas.2017.8325150 |
| Popis: | Long-term neural recording which can consistently provide good signal-to-noise ratio (SNR) performance over time is important for stable operation of neuroprosthetic systems. This paper presents an analysis for the SNR optimization in a changing environment which causes variations in the tissue-electrode impedance, Zte. Based on the analysis result, a neural recording amplifier (NRA) is developed employing the SNR optimization technique. The NRA can adaptively change its configuration for in situ SNR optimization. The SNR is improved by 4.69% to 23.33% as Zte changes from 1.59 MQ to 31.8 MQ at 1 kHz. The NRA is fabricated in a 0.18-μm standard CMOS process and operates at 1.8-V supply while consuming 1.6 μA It achieves an input-referred noise of 4.67 μVrms when integrated from 1 Hz to 10 kHz, which leads to the NEF of 2.27 and the NEF2VDD of 9.28. The frequency reponse is measured with a high-pass cutoff frequency of 1 Hz and a low-pass cutoff frequency of 10 kHz. The midband gain is set to 40 dB while occupying 0.11 mm2 of a chip area. |
| Databáze: | OpenAIRE |
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