Jie Yin;Xiangyi Liu;Yinuo Zhang;Xufeng Liao;Lianxi Liu
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引用次数: 0
摘要
本文提出了一种用于生物传感器的低通道失配ExG模拟前端(AFE)。提出了一种抑制通道间输入阻抗失配的正交嵌套斩波技术。正交嵌套斩波器的第一级具有相同的频率,以确保每个通道具有相同的输入阻抗。二级斩波器采用沃尔什-阿达玛码控制,可以抑制信道间的信号串扰,降低系统的调制频率。此外,提出了一种基于逐次逼近(SA)逻辑的输入电容校准技术,通过逐步校准每个通道的输入电容,有效地抑制了增益失配。所提出的AFE采用65纳米CMOS工艺制造,核心面积为$0.95\times 0.9$ mm2。测量结果表明,在1.2 v电源电压下,所提出的AFE每通道平均消耗$2.6~\mu $ W。输入阻抗大于1.96 G $\Omega $,通道间输入阻抗失配为0.21%. The gain range is 26–46 dB, and the gain mismatch among channels is only 0.11%. The total common-mode rejection ratio (CMRR) is increased to 91 dB. The proposed AFE can clearly acquire electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG).
A Low-Channel-Mismatch ExG AFE Based on Orthogonal Nested-Chopper and Successive-Approximation Input Capacitance Calibration
This article proposes a low-channel-mismatch ExG analog front end (AFE) for biosensors. An orthogonal nested-chopper technique is proposed to suppress the mismatch in interchannel input impedance. The first stage of the orthogonal nested chopper has the same frequency to ensure that each channel has the same input impedance. The second-stage chopper is controlled by Walsh-Hadamard codes, which can suppress the signal crosstalk among channels and lower the system’s modulation frequency. In addition, an input capacitance calibration technique based on successive-approximation (SA) logic is proposed, effectively suppressing the gain mismatch by gradually calibrating the input capacitance of each channel. The proposed AFE was fabricated in a 65-nm CMOS process, and the core area is $0.95\times 0.9$ mm2. The measured results show that the proposed AFE consumes an average of $2.6~\mu $ W per channel at a 1.2-V supply voltage. The input impedance is greater than 1.96 G$\Omega $ , and the mismatch in input impedance among channels is 0.21%. The gain range is 26–46 dB, and the gain mismatch among channels is only 0.11%. The total common-mode rejection ratio (CMRR) is increased to 91 dB. The proposed AFE can clearly acquire electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG).
期刊介绍:
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