Bias-tunable IR photodetector based on asymmetrically doped GaAs/AlGaAs double-quantum-well nanomaterial for remote temperature sensing

摘要

We designed, fabricated, and characterized multi-color IR photodetectors with asymmetrical doping of GaAs/AlGaAs double quantum wells (DQW). We measured and analyzed spectral and noise characteristics to evaluate feasibility of these photodetectors for remote temperature sensing at liquid nitrogen temperatures. The bias voltage controls the charge distribution between the two wells in a DQW unit and provides effective tuning of IR induced electron transitions. We have found that the responsivity of our devices is symmetrical and weakly dependent on the bias voltage because the doping asymmetry compensates the effect of dopant migration in the growth direction. At the same time, the asymmetrical doping strongly enhances the selectivity and tunability of spectral characteristics by bias voltage. Multicolor detection of our QWIP is realized by varying the bias voltage. Maximum detection wavelength moves from 7.5 μm to 11.1 μm by switching applied bias from -5 V to 4 V. Modeling shows significant dependence of the photocurrent ratio on the object temperature regardless of its emissivity and geometrical factors. We also experimentally investigated the feasibility of our devices for remote temperature sensing by measuring the photocurrent as a response to blackbody radiation with the temperature from 300°C to 1000°C in the range of bias voltages from -5 V to 5 V. The agreement between modelling and experimental results demonstrates that our QWIP based on asymmetrically doped GaAs/AlGaAs DQW nanomaterial is capable of remote temperature sensing. By optimizing the physical design and varying the doping level of quantum wells, we can generalize this approach to higher temperature measurements. In addition, continuous variation of bias voltage provides fast collection of large amounts of photocurrent data at various biases and improves the accuracy of remote temperature measurements via appropriate algorithm of signal processing.