In large area imaging, an active pixel sensor (APS) is commonly used to achieve high signal-to-noise ratio (SNR) through an in-pixel thin-film transistor (TFT) amplifier. Since there is a trade-off between SNR and spatial resolution, the challenge of achieving both high resolution and high SNR remains.To resolve this, a 3-D one-TFT APS architecture is proposed, and a 256 Â 256 image sensor array with a fill factor of $100%, a high gain of >10 3 , and a high resolution of 110 ppi is presented.

Chronical cardiovascular decreases such as hypertension requires real-time and continuous monitoring of blood pressures (BPs). Pulse wave that contains critical and ample information on cardiovascular dynamics is a direct vital sign to extract blood pressure and therefore an epidermal wearable device enabling real-time acquisition of pulse waves becomes necessary. In this work, we propose and study a flexible pulse wave sensory array aiming for real-time wearable pulse wave acquisition with robustness. A piezoelectric sensor together with a thin-film transistor-based sensor interface circuit is used to detect multiple pulse waveforms at the location of radial artery, which are input to a convolutional neural network (CNN) for a deep-learning BP estimation model training. The estimated BPs are assessed by a correlation study with the BPs measured by conventional sphygmomanometers. Our study shows a reasonable accuracy of mean deviation (MD) and standard deviation (STD) of 2.84±7.53 mmHg for systolic BP (SBP) and 0.88±7.06 mmHg for diastolic BP (DBP).