An improved analytical model for quantifying the full well capacity in pinned photodiode (PPD) CMOS image sensors is proposed. The model captures the characteristics of the realistic technology-induced vertical doping nonuniformity in photon sensing N-type area of the PPD structure and the voltage dependency of the PPD capacitance, respectively, both of which were neglected in the existing works. Excellent agreement between measured and predicted data shows that the proposed model fits a wider range of technology conditions for a wider spectra responding compared to the up-to-date reported model.

This paper presents a time-resolved (TR) CMOS image sensor (CIS) using two-tap near-infrared (NIR) lock-in pixels with background light (BGL) cancelling capability for remote heart rate (HR) detection. Aimed to the application, the sensor features a 1024 (V) x 1280 (H) spatial resolution with 412 (V)x 1280 (H) region-of-interest-readout (ROI-readout) at 30 fps, a high sensitivity of 134.8 ke⁻/lux · s, and an ultra-low temporal random noise of 1.13 erms⁻ at analog gain of 64. A maximum charge modulation ratio of 90.1% is obtained for realizing lock-in operation by means of in-pixel lateral electric field charge modulator (LEFM). The remote HR detection is achieved by capturing the heart-beat-induced temporal variation of the oxy-hemoglobin (HbO₂) concentration by the developed TR CIS with NIR lock-in technique, and larger than 98% detection accuracies are attained under both non-visible-light condition and fluctuant brightness ambient-light environments. This remarkable feature demonstrates the superiority of the proposed method for driver monitoring applications in particular.

A 7.1- μm-pitch lock-in pixel (LIP) CMOS image sensor (CIS) that is capable of simultaneously phasing with two different near-infrared-band (NIR-band) light pulses is developed for remote physiological measurement, particularly a heart rate variability (HRV) signal monitor. The proposed dual NIR-band lock-in operation is realized by a four-tap-wise charge modulation with adjacent two-tap LIPs by means of a column-interdigital pixel driving scheme combining with in-LIP lateral electric field modulators (LEFMs). For attaining the weak HRV signal, the implemented CIS achieves a subelectron temporal noise of 0.67 e ^- _rms at 64 times correlated multiple sampling (CMS) and a high charge modulation contrast (MC) up to 96% at maximum. These remarkable properties primarily result from the in-LIP dark current and LEFM controlling optimizations proposed in device domain. The experimental results show that the developed CIS exhibits not only a 98.5% accuracy in HRV signal capture with -39.1-dB signal-to-background ratio (SBR) against a fluctuant brightness ambient light but also a moderate artificial motion robustness benefited from the proposed dual NIR-band lock-in technique.

A time-resolved CMOS image sensor (CIS) using two-tap near infrared (NIR) lock-in pixels with background light (BGL) cancelling capability for non-contact heart rate (HR) detection is presented. Aimed to the application, the sensor performances of 1.1e ^- _rms temporal random noise and 90.1% maximum charge modulation ratio are achieved by using low noise readout circuit and in-pixel lateral electric field charge modulator (LEFM) techniques. The non-contact HR detection with accuracies of more than 98% is implemented by the developed sensor under both non-visible-light and fluctuant ambient-light brightness conditions. This remarkable performance indicates the superiority of the proposed technique for automotive security applications in particular.

Monitoring tissue metabolic and perfusion information such as oxygenation and hemoglobin concentration plays a crucial role in healthcare and therapy. Traditional methods commonly used to obtain such information include using a pulse oximeter¹ attached to the patient’s finger or employing near-infrared spectroscopy (NIRS)² using probes. However, neither of them can provide noncontact measurements nor cover a wide field of view. Spatial frequency domain imaging (SFDI) is an imaging technique that projects structured light with different spatial frequencies and phases onto diffusely reflecting objects to quantify absorption and reduced scattering coefficients.^3,4 SFDI is a noninvasive, noncontact, real-time, wide-field, quantitative imaging technique. Compact SFDI systems designed for bedside use have been employed in various experiments, including predicting diabetic foot ulcer,^{5 – 7} oxygenation measurement during reconstructive breast surgery,⁸ monitoring skin cancer,⁹ imaging and staging pressure ulcers,¹⁰ among others. SFDI is a particularly promising method for diagnosing burn severity since changes in reduced scattering measured using SFDI have been shown to correlate with histologically verified changes in collagen as a function of burn severity. These changes can be detected as early as 24 h after the burn.^11,12 This change in scattering, along with tissue oxygen saturation (${\mathrm{StO}}_2$) and total hemoglobin concentration ($c_{\mathrm{tHB}}$) derived from absorption coefficients, enable clinicians to make improved treatment decisions and can sometimes lead to early recovery for the patient.¹³ To accurately quantify tissue metabolic information, particularly the concentrations of oxy/deoxy hemoglobin, we require SFDI measurements at two or more wavelengths. However, since the presence of melanin in the epidermis can complicate the measurement results of other tissue chromophores, at least three wavelengths (as many as the number of chromophores) are generally required to achieve accurate determination of oxy and deoxyhemoglobin concentrations.