Adding vision capabilities to wireless sensors nodes (WSN) for the Internet-of-Things requires imagers working at ultra-low power (ULP) in nanometer CMOS systems-on-chip (SoCs). Such performance can be obtained with time-based digital pixel sensors (DPS) working at ultra-low voltage (ULV), at the expense of lower dynamic range, higher fixed-pattern noise (FPN) and thus poorer image quality. To address this problem, three key techniques were developed in this work for DPS pixels: wide-range adaptive body biasing, low-gating of the 2-transistor in-pixel comparator and digital readout performing delta-reset sampling with low switching activity and robust timing closure. These concepts were demonstrated by designing and fabricating a 128 × 128 CMOS image sensor array in a 65 nm low-power CMOS logic process. Operating at 0.5 V, it features an FPN of 0.66%, a dynamic range of 42 dB and a fill factor of 57% with a 4 μm pixel pitch, while consuming only 17 pJ/(frame.pixel) and 8.8 μW at 32 fps. These performances combined with the small silicon area of 0.69 mm2makes the imager perfectly suitable for integration in ULP SoCs, targeting WSN applications.
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Couniot et al. (2015) studied this question.
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