In this paper, multiple Fano resonances preferred in the refractive index sensing area are achieved based on sub-wavelength metal-insulator-metal (MIM) waveguides. Two slot cavities, which are placed between or above the MIM waveguides, can support the bright modes or the dark modes, respectively. Owing to the mode interferences, dual Fano resonances with obvious asymmetrical spectral responses are achieved. High sensitivity and high figure of merit are investigated by using the finite-difference time-domain (FDTD) method. In view of the development of chip-scale integrated photonics, two extra slot cavities are successively added to the structure, and consequently, three and four ultra-sharp Fano peaks with considerable performances are obtained, respectively. It is believed that this proposed structure can find important applications in the on-chip optical sensing and optical communication areas.

In this paper, multiple Fano resonances based on semi-ring-rectangular composite cavity (SRRCC) structure are numerically investigated and explained by FDTD method and MICMT method. High refractive-index sensitivities and high figure of merits (FOMs) are obtained in SRRCC system, and thus it may be preferred in the chip-scale optical sensing area. Besides, the phase response and the delay time within the Fano resonant windows are also investigated. Additionally, two more Fano resonances with high sensitivities and FOMs emerge through adding a new semi-ring cavity. These structures show widely applications in the sensing area and promote development of on-chip integrated photonics.

The micro-structure with submicron-scale precision so far as to minimum feature size of 1.5 μm can be achieved at faster fabrication speed. This rapid prototyping fabrication method take advantage of combining of maskless lithography technique based on digital mirror device (DMD) and photo-curable resin which has a broad range of use in three-dimensional printing (3D Printing). To illustrate the feasibility of this method, a kind of Dammann grating was chosen to be fabricated. By a series of experiments and optimization designs, we analyzed the characteristics of the photo-curable resin, quantified the dose modulation of lithography and redesigned the mask pattern. Consequently, the Dammann grating with 50% diffraction efficiency was fabricated successfully which can not only guarantee the precision and but also keep up the fabrication speed. This work demonstrates the potential of this method to rapidly and directly manufacturing binary-optics elements or structure in nano-scale based on photo-curable resin and DMD-based maskless lithography.

In this study, a method is proposed for the rapid fabrication of a hexagonal compound eye microlens array (HCE-MLA) using maskless lithography technique based on digital micromirror device (DMD), in which a hexahedron array is lithographed, and subsequently, a microlens structure is lithographed layer by layer upon the hexahedron. Owing to the high filling rate of the hexagon and the aspheric surface of the paraboloid profile of the microlens, after the distribution of the required exposure dose of HCE-MLA was calculated based on dose modulation, a series of circular pattern with different radius could be obtained by equal-arc-mean slicing strategy that can adapt to the variable curvature of the target contour and improve its reconstruction precision. Then, after a hexagonal photoresist island was fabricated on the substrate, and the dose accumulated over multiple exposures, the required exposure dose profiles were reconstructed on the hexagonal photoresist island. Finally, a high-quality HCE-MLA with a smooth surface was fabricated via thermal reflow. The fabrication of the designed HCE-MLA using this method was demonstrated in experiments. The results indicate that the actual profile of HCE-MLA shows good agreement with the design profile and that simultaneously increasing the number of the slicing layers can improve the quality of the profile. The research indicated that this method is feasible and convenient for the fabrication of high-quality HCE-MLA.

This paper presents a semi-automatic approach to segmentation of liver parenchyma from 3D computed tomography (CT) images. Specifically, liver segmentation is formalized as a pattern recognition problem, where a given voxel is to be assigned a correct label - either in a liver or a non-liver class. Each voxel is associated with a feature vector that describes image textures. Based on the generated features, an Extreme Learning Machine (ELM) classifier is employed to perform the voxel classification. Since preliminary voxel segmentation tends to be less accurate at the boundary, and there are other non-liver tissue voxels with similar texture characteristics as liver parenchyma, morphological smoothing and 3D level set refinement are applied to enhance the accuracy of segmentation. Our approach is validated on a set of CT data. The experiment shows that the proposed approach with ELM has the reasonably good performance for liver parenchyma segmentation. It demonstrates a comparable result in accuracy of classification but with a much faster training and classification speed compared with support vector machine (SVM).