We report an area- and energy-efficient integrated circuit architecture of a 128-channel Δ-modulated ΔΣ analog front-end (Δ-ΔΣ AFE) for 1024-channel 3-D massive-parallel neural recording microsystems. Our platform has adopted a modularity of 128 channels and consists of eight multi-shank neural probes connected to individual AFEs through interposers in a small form factor. In order to reduce both area and energy consumption in the recording circuits, we implemented a spectrum equalization scheme to take advantage of the inherent spectral characteristics of neural signals, where most of the energy is confined in low frequencies and follows a ~1/f curve in the spectrum. This allows us to implement the AFE with a relaxed dynamic range by ~30 dB, thereby contributing to the significant reduction of both energy and area without sacrificing signal integrity. The Δ-ΔΣ AFE was fabricated using 0.18-μm CMOS processes. The single-channel AFE consumes 3.05 μW from 0.5 and 1.0 V supplies in an area of 0.05 mm 2 with 63.8-dB signal-to-noise-and-distortion ratio, 3.02 noise efficiency factor (NEF), and 4.56 NEF 2 V DD . We also have achieved an energy-area product, a figure-of-merit most critical for massiveparallel neural recording systems, of 6.34 fJ/C · s · mm 2 .
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Park et al. (2017) studied this question.
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