Avalanche photodiodes (APDs) have been widely used in optical communications, radar imaging and single photon detection fields, etc.. Compared with PIN photodiodes, the internal gain of APDs provide higher sensitivity and signal-to-noise ratio. The APDs gain come from the collision ionization of carriers, which is a random process and causes excess noise in the APDs, therefore, study of the excess noise factor of the detectors is of great significance to the performance improvement of APDs. This paper uses the direct power method to test the excess noise voltage spectral density of the InGaAs/InP APD, uses the source meter to measure the light and dark currents to calculate the gain. The relative intensity noise of the laser and the system impedance are calculated by linear fitting. The effect of measurement temperature and optical power on excess noise factor is investigated and discussed.
The performance of InP/InGaAs SPAD detectors depends on the electrical field distribution in their multiple layers. In the conventional separate absorption, grading, charge and multiplication (SAGCM) structure, the major function of the charge layer is to confine the electrical fields, so the charge layer’s parameter design is very important for any enhanced SPAD detectors. Normally the sheet density equals to doping concentration times thickness is considered as one of the key factors for the device design, however, even with the same sheet density, there are different combinations of doping densities and thicknesses. Our calculations show that with the same sheet density of charge layer, the one with higher doping concentration has higher electrical fields in both multiplication and absorption layers, then has lower breakdown and punch-through voltages. The results were also verified by the experimental measurements.
Estimating the number of people in images is an important task in computer vision, and has a wide range of applications, such as video surveillance, traffic monitoring, public safety and urban planning. The task of crowd counting faces many challenges, e.g. extremely scale variations in extremely dense crowd scenarios, severe occlusion, and perspective distortion. We have studied a new type of multi-granularity full convolutional network as an effective solution for crowd counting. We not only designed the parallel multi-receptive field module to learn the mapping from the crowd images to the density maps, but also introduced the skip-connection mechanism to better train the mapping to improve the quality of the estimated density map, which is critical for accurate crowd counting. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than the most advanced method.
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