Co-implantation with conventional spike anneal solutions for 45 nm n -type metal-oxide-semiconductor ultrashallow junction formation Abstract. Local resistance profiles of ultra shallow boron and arsenic implanted into silicon with energies of 2.0 and 4.0 keV and doses of 2.0×10 15 and 1.0×10 15 ions/cm 2 activated by a combination of conventional spike lamp and laser annealing processes were measured by scanning spreading resistance microscope (SSRM) with a depth resolution of less than 10 nm. The lowest local resistance at the low resistance region in 2.0 keV boron implanted silicon with 1050 ºC spike lamp annealing followed by 0.35 kW/mm 2 laser annealing was half of that without laser annealing. The lowest local resistance at the low resistance region in the arsenic implanted silicon activated by 1050 ºC spike lamp annealing followed by 0.39 kW/mm 2 laser annealing was 74 % lower than that followed by 0.36 kW/mm 2 laser annealing. The lowest local resistances at the low resistance regions in the arsenic implanted silicon with 0.36 and 0.39 kW/mm 2 laser annealing followed by 1050 ºC spike lamp annealing were 41 and 33 % lower than those with spike lamp annealing followed by laser annealing. Laser annealing followed by spike lamp annealing could suppress the diffusion of the impurities and was suitable for making the ultra shallow and low resistance regions.

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The paper proposes a novel method for estimating the optical flows from the sequentially captured images with using their own color information. The gradient method is well known as one of the conventional methods to estimate the flows, and then the spatial and temporal derivative of the images are used in the method. Since the color images have richer information than the monochrome ones, they should contribute for estimating the more precise optical flows. In our approach, the color derivative vector (CDV) is introduced to bring out the information in the color images for the optical flow estimation. The CDV is derived from the derivatives of color images, and the optimal CDV provides the concrete weighting values of the RGB data. The optimal CDV is obtained with using the eigenvalues and the eigenvectors of the matrix consisting of the spatial and temporal color derivatives.