Imaging interstitial iron concentrations in boron-doped crystalline silicon using photoluminescence

Imaging the band-to-band photoluminescence of silicon wafers is known to provide rapid and high-resolution images of the carrier lifetime. Here, we show that such photoluminescence images, taken before and after dissociation of iron-boron pairs, allow an accurate image of the interstitial iron concentration across a boron-doped p-type silicon wafer to be generated. Such iron images can be obtained more rapidly than with existing point-by-point iron mapping techniques. However, because the technique is best used at moderate illumination intensities, it is important to adopt a generalized analysis that takes account of different injection levels across a wafer. The technique has been verified via measurement of a deliberately contaminated single-crystal silicon wafer with a range of known iron concentrations. It has also been applied to directionally solidified ingot-grown multicrystalline silicon wafers made for solar cell production, which contain a detectible amount of unwanted iron. The iron images on these wafers reveal internal gettering of iron to grain boundaries and dislocated regions during ingot growth.D.M. is supported by an Australian Research Council QEII Fellowship. The Centre of Excellence for Advanced Silicon Photovoltaics and Photonics at UNSW is funded by the Australian Research Council

Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003

Observations of nitrous acid (HONO) by laser-induced fluorescence (LIF) at the South Pole taken during the Antarctic Troposphere Chemistry Investigation (ANTCI), which took place over the time period of Nov. 15, 2003 to Jan. 4, 2004, are presented here. The median observed mixing ratio of HONO 10 m above the snow was 5.8 pptv (mean value 6.3 pptv) with a maximum of 18.2 pptv on Nov 30th, Dec 1st, 3rd, 15th, 17th, 21st, 22nd, 25th, 27th and 28th. The measurement uncertainty is ±35%. The LIF HONO observations are compared to concurrent HONO observations performed by mist chamber/ion chromatography (MC/IC). The HONO levels reported by MC/IC are about 7.2 ± 2.3 times higher than those reported by LIF. Citation: Liao, W., A. T. Case, J. Mastromarino, D. Tan, and J. E. Dibb (2006), Observations of HONO by laser-induced fluorescence at the South Pole during ANTCI 2003, Geophys. Res. Lett., 33, L09810, doi:10.1029/2005GL025470

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction