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An Inverter-Based Amplifier Structure for Neural Signal Recording with an NEF of 1.28 and Area-per-Channel of 0.06mm2

机译:基于逆变基的放大器结构,用于NEF为1.28和0.06mm2的每声道的NEF

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The Noise-Efficiency-Factor (NEF) and area efficiency of the neural signal amplifier has been continuously improved over the years to achieve prolonged recording lifetime and increased channel count. By far, the most energy efficient neural amplifiers are configured based on fully-differential inverter structures yielding double input transconductance and reduced NEF with a theoretical limit of $sqrt{2}$ [1– 5]. Recently, a stacking technique has been superimposed on conventional inverter-based structures to further push the NEF below 1 by reusing the bias currents through stacked input stages [1], [2]. Unfortunately, the output dynamic range, maximum allowable bias current, and thermal noise floor in a stacked structure are all compromised due to smaller voltage headroom. Moreover, large isolation capacitors are required at both input and output nodes of each stacked stage for DC voltage separation, so area expands significantly as more stages are stacked [1].
机译:多年来,神经信号放大器的噪声效率因数(NEF)和面积效率在延长记录寿命和增加的通道计数中,在多年来上持续提高。 到目前为止,最能节能的神经放大器基于完全差分逆变器结构来配置双输入跨导和减少NEF,其理论限制为$ SQRT {2} $ [1-5]。 最近,通过通过堆叠输入阶段重复使用偏置电流[1],[2],叠加在基于逆变器的基于逆变器的结构上以进一步推动NEF的堆叠技术,以进一步推动NEF。 不幸的是,由于较小的电压净空,输出动态范围,堆叠结构中的最大允许偏置电流和热噪声底板都受到损害。 此外,对于DC电压分离的每个堆叠级的两个输入和输出节点都需要大的隔离电容,随着更多阶段堆叠的情况下,区域显着膨胀[1]。

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