This article presents a pixelated monolithic CMOS photoplethysmography (PPG) sensor. The spiking neural network (SNN)-inspired architecture, which includes an unbiased photodiode (PD)-based light-to-digital converter (LDC), efficiently converts spatial features of PPG signals to multiple pixelated outputs. The sensor consists of 144 light-sensing neurons (LSNs) and simultaneously generates 1 b 12 row/12 column-wise pixelated outputs. The spatial features of the PPG signals are thoroughly studied with measurement results. Moreover, ambient light canceling with independent component analysis (ICA) is demonstrated as a key application of the proposed sensor. The sensor chip achieves 114 dB dynamic range while consuming 23.3 μW at 1-V supply voltage.
This article was approved by Associate Editor David Stoppa. This work was supported by the National Research Foundation (NRF) of Korea under Grant 2022M3H4A1A03067131, Grant 2021R1A2C4002496, Grant 2019R1A5A1027055, and Grant 2020M3F3A2A01082593.
