A novel data processing method for light-addressable potentiometric sensors for the narrow bias range required

A light-addressable potentiometric sensor (LAPS) is a type of bio-sensor based on the semiconductor field effect and modulated light, driven by the advantages of a wide detection range, low fabrication costs, support for miniaturization, etc. The conventional LAPS data processing methods rely on I-V characteristic curves, where a wide bias voltage range (over 1 V) is required to achieve a complete I-V curve, including the accumulation, depletion, and inversion regions in a single measurement process. The wide bias voltage range places high demands on the power supply of the measurement system and is not conducive to miniaturization. In this paper, a novel data processing method for LAPSs based on Gaussian fitting is proposed, and the pH sensing effect of the sensor under this method is investigated. Under the new data processing method, the linearity of the LAPS was 98.2%, the sensitivity was 5.378 points/pH, and the average repetition rate was 1.27%, where the bias voltage range was 0.07 V. The experimental results showed that the new data processing method could help the LAPS to obtain an acceptable pH sensing effect and a narrower bias voltage range compared with these properties for a LAPS operating using the traditional data processing methods. The data processing method based on Gaussian fitting shows the potential to be applied to the design of low-power, miniature LAPSs.