A 72-Channel Resistive-and-Capacitive Sensor-Interface Chip With Noise-Orthogonalizing and Pad-Sharing Techniques
Xiangdong Feng (Nanhu Brain-Computer Interface Institute, Zhejiang University - Hangzhou)
Yuxuan Luo (Zhejiang University - Hangzhou)
Tianyi Cai (Zhejiang University - Hangzhou)
Yangfan Xuan (Zhejiang University - Hangzhou)
Yunshan Zhang (Microaiot)
Yili Shen (Zhejiang University - Hangzhou)
Changgui Yang (Zhejiang University - Hangzhou)
Qijing Xiao (Zhejiang University - Hangzhou)
Sijun Du (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Bo Zhao (Nanhu Brain-Computer Interface Institute, Zhejiang University - Hangzhou)
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Abstract
The growing trend of the Internet of Things (IoT) involves trillions of sensors in various applications. An extensive array of parameters need to be gathered concurrently with high-precision, low-cost, and low-power sensor nodes, such as resistive (R) and capacitive (C) sensors. Single-chip channel fusion can be an effective solution, while it is challenging to suppress the noise and integrate massive I/O pads. However, conventional oversampling noise-shaping methods increase power consumption, which fails to meet the demand of long-term monitoring applications. In addition, existing R/C sensor-interface chips require a pair of I/O pads for each sensor, where the pad frame dominates the overall chip area in massive-channel integration. In this work, we demonstrate a 72-channel R&C sensor-interface chip for proximity-and-temperature sensing. A noise-orthogonalizing technique is proposed to eliminate the quantization noise at the signal frequencies, achieving an energy efficiency of 19.1 pJ/step/channel. Moreover, a pad-sharing technique is proposed to reduce the number of I/O pads by half, enabling 72 sensors to be read by 36 pairs of I/O pads. The chip is fabricated by 65-nm CMOS technology, and measurement results show resolutions of 286 Omega and 162 fF, respectively. The power consumption and die area are reduced to 0.74 mu text{W} /Channel and 0.038 mm2/Channel, respectively.