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A study on temperature characteristics of green silicon photodetector

Fu X., Yao S., Tao G., De M.

Optica Applicata

2010

Vol. 40, nr 4

935--941

Green silicon photodetector is successfully developed on the substrate of n-type single-crystal (100) silicon. To improve its performance, the detector is optimized by optimizing the p-n junction depth xj and the thickness of antireflection layer to reduce dark current, shorten response time and increase sensitivity. The spectrum response SNR can be over 104 within the wavelength range of 500-600 nm and the peak of spectral responsivity is 0.48 A/W at about 520 nm. The temperature characteristics of the dark current at reverse bias and photocurrent at zero bias are emphatically investigated. Firstly, the temperature behavior of dark current at 10 V reverse bias voltage and temperature range of 253-323 K is studied. Results show that dark current is dominated by generation-recombination current Igr the temperature range of 253-283 K and it is dominated by traps tunneling current Itt at the temperature range of 283-323 K. Secondly, the temperature behavior of photocurrent at zero bias and temperature range of 213-353 K is discussed. Results show that photocurrent increases as temperature increases below room temperature and almost holds the line over room temperature. Consequently, photodetector fulfils quality requirements.

An Improved Interactive Color Image Segmentation Using Region-Based Graph Cuts

Yuan T., Cheng W., Tao G., Lijun L.

Machine Graphics and Vision

2009

Vol. 18, No. 4

477-488

The problem of efficient interactive extraction of a foreground object in a complex environment is the primary one in image processing and computer vision. The segmentation method based on graph cuts has been studied over the recent years. There are two main drawbacks of these studies: decrease in performance when the foreground and the background have similar colors, and long computing time when the image is large. In this paper, we present a new foreground objects extraction method using a region-based graph cuts algorithm. The image is pre-segmented into a large number of small partitions using the mean shift (MS) method. We use the partitions to represent the nodes in the graph instead of pixels. This approach can reduce the optimization time, which is closely related to the number of nodes and edges in the graph. Compared with the pixel-based method, our method can yield an excellent performance and exhibit a faster speed.