Fully Differential Dynamic Neural Amplifier: Preventing Saturation from Artifacts and Breaking the Gain-Bandwidth Trade-Off

Neural recording is fundamental to advancements in neuroscience and the development of brain-computer interfaces. Central to this process is the neural amplifier, a critical component that directly influences the quality and fidelity of neural signal acquisition. The amplifier’s performance determines the noise level contaminating neural signals and ensures adequate amplification for accurate analog-to-digital conversion (ADC). However, conventional neural amplifiers face significant challenges. They typically require a constant biasing current, leading to inefficient power consumption, and are limited by a constrained gain-bandwidth product due to power budget restrictions. This paper addresses these challenges by delving into the essential requirements for neural amplifiers and discussing the limitations of traditional designs. We introduce a dynamic amplifier that not only surpasses the conventional gain-bandwidth trade-off but also effectively mitigates saturation issues caused by stimulation or motion artifacts. Furthermore, the proposed amplifier exhibits significantly lower thermal and $1 / \mathrm{f}$ noise compared to traditional static amplifiers. Our simulation results demonstrate that the proposed neural amplifier consumes less than 200 nW of power, with a signal-to-noise-and-distortion ratio (SNDR) of 82.6 dB at a $\mathrm{5 k H z}$ bandwidth. The amplifier also achieves an input-referred noise of $4.54 \mu V_{\text {rms }}$. Additionally, the noise-efficient factor is 1.1, highlighting the amplifier’s noise performance and its suitability for high-fidelity neural signal acquisition.