A Systematic Design Flow for Compact and Efficient Analog Front-Ends in Closed-Loop Neuromodulation Systems

In the growing field of closed-loop neuromodulation, designing power-and area-efficient analog front-ends (AFEs) for neural recording poses a significant challenge. Thus, an effective design flow is needed to ensure system's hardware efficiency and to automate the design process. This paper presents a methodological framework for designing capacitively coupled, chopper-stabilized AFEs for recording neural and other biosignals. Our study addresses the challenges encountered at each design stage of low-noise amplifiers (LNAs), providing unique insights into practical solutions. Two distinct approaches are introduced to enhance capacitance density in the compensation capacitor of the second LNA, given its significant contribution to the overall area. The first method involves adjustments to common-mode voltage, resulting in a 29% reduction in AFE area. The second approach employs active capacitor multiplication, achieving a 33% reduction in the AFE area. This design was simulated in a 65nm low-power CMOS process.