Abstract: A method of manufacturing a semiconductor device includes forming a first layer of a first planarizing material over a patterned surface of a substrate, forming a second layer of a second planarizing material over the first planarizing layer, crosslinking a portion of the first planarizing material and a portion of the second planarizing material, and removing a portion of the second planarizing material that is not crosslinked. In an embodiment, the method further includes forming a third layer of a third planarizing material over the second planarizing material after removing the portion of the second planarizing material that is not crosslinked. The third planarizing material can include a bottom anti-reflective coating or a spin-on carbon, and an acid or an acid generator. The first planarizing material can include a spin-on carbon, and an acid, a thermal acid generator or a photoacid generator.

Abstract: Methods and semiconductor devices are provided. A method includes determining a location of a polyimide opening (PIO) corresponding to an under-bump metallization (UBM) feature in a die. The die includes a substrate and an interconnect structure over the substrate. The method also includes determining a location of a stacked via structure in the interconnect structure based on the location of the PIO. The method further includes forming, in the interconnect structure, the stacked via structure comprising at most three stacked contact vias at the location of the PIO.

Abstract: The disclosure provides a method of active immunotherapy for a cancer patient, comprising administering vaccines against Globo series antigens (i.e., Globo H, SSEA-3 and SSEA-4). Specifically, the method comprises administering Globo H-CRM197 (OBI-833/821) in patients with cancer. The disclosure also provides a method of selecting a cancer patient who is suitable as treatment candidate for immunotherapy. Exemplary immune response can be characterized by reduction of the severity of disease, including but not limited to, prevention of disease, delay in onset of disease, decreased severity of symptoms, decreased morbidity and delayed mortality.

Abstract: An analysis method and an electronic apparatus for breast image are provided. The method includes the following steps. One or more breast ultrasound images are obtained. The breast ultrasound images are used for forming a three-dimensional (3D) breast model. A volume of interest (VOI) in the breast ultrasound image is obtained by applying a detection model on the 3D breast model. The VOI is compared with a tissue segmentation result. The VOI is determined as a false positive according to a compared result between the VOI and the tissue segmentation result. The compared result includes that the VOI is located at a glandular tissue based on the tissue segmentation result. In response to the VOI being located in the glandular tissue of the tissue segmentation result, the VOI is compared with the lactiferous duct in the 3D breast model.

Abstract: A method of manufacturing a semiconductor device includes forming a first layer of a first planarizing material over a patterned surface of a substrate, forming a second layer of a second planarizing material over the first planarizing layer, crosslinking a portion of the first planarizing material and a portion of the second planarizing material, and removing a portion of the second planarizing material that is not crosslinked. In an embodiment, the method further includes forming a third layer of a third planarizing material over the second planarizing material after removing the portion of the second planarizing material that is not crosslinked. The third planarizing material can include a bottom anti-reflective coating or a spin-on carbon, and an acid or an acid generator. The first planarizing material can include a spin-on carbon, and an acid, a thermal acid generator or a photoacid generator.

Abstract: A switch device includes a P-type substrate, a first gate structure, a first N-well, a shallow trench isolation structure, a first P-well, a second gate structure, a first N-type doped region, a second P-well, and a second N-type doped region. The first N-well is formed in the P-type substrate and partly under the first gate structure. The shallow trench isolation structure is formed in the first N-well and under the first gate structure. The first P-well is formed in the P-type substrate and under the first gate structure. The first N-type doped region is formed in the P-type substrate and between the first gate structure and the second gate structure. The second P-well is formed in the P-type substrate and under the second gate structure. The second N-type doped region is formed in the second P-well and partly under the second gate structure.

Abstract: The disclosure is directed to an object detection method using a CNN model and an object detection apparatus thereof. In an aspect, the object detection method includes generating a sensor data; processing the sensor data by using a first object detection algorithm to generate a first object detection result; processing the first object detection result by using a plurality of stages of sparse update mapping algorithm to generate a plurality of stages of updated first object detection result; processing a first stage of the stages of updated first object detection result by using a plurality of stages of spatial pooling algorithm between each of stages of sparse update mapping algorithm; executing a plurality of stages of deep convolution layer algorithm to extract a plurality of feature results; and performing a detection prediction based on a last-stage feature result.

Abstract: In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Abstract: The disclosure is directed to an object detection method using a CNN model and an object detection apparatus thereof. In an aspect, the object detection method includes generating a sensor data; processing the sensor data by using a first object detection algorithm to generate a first object detection result; processing the first object detection result by using a plurality of stages of sparse update mapping algorithm to generate a plurality of stages of updated first object detection result; processing a first stage of the stages of updated first object detection result by using a plurality of stages of spatial pooling algorithm between each of stages of sparse update mapping algorithm; executing a plurality of stages of deep convolution layer algorithm to extract a plurality of feature results; and performing a detection prediction based on a last-stage feature result.

Abstract: In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Abstract: An object detecting device, an object detecting method and a non-transitory computer-readable medium are provided. The object detecting method includes the following steps: A classifier generates a current color image and a current gray scale image. The classifier generates an initial characteristic pattern from the current color image via a neural network algorithm. The classifier adjusts a current dimension of the initial characteristic pattern to generate an adjusted characteristic pattern according to a gray scale image dimension of the current gray scale image. The classifier concatenates the adjusted characteristic pattern and the current gray scale image to calculate a class confidence. The classifier determines whether the class confidence is larger than a confidence threshold, and outputs a current classification result if the class confidence is larger than the confidence threshold. A storage device stories the current classification result.

Abstract: An object detecting device, an object detecting method and a non-transitory computer-readable medium are provided. The object detecting method includes the following steps: A classifier generates a current color image and a current gray scale image. The classifier generates an initial characteristic pattern from the current color image via a neural network algorithm. The classifier adjusts a current dimension of the initial characteristic pattern to generate an adjusted characteristic pattern according to a gray scale image dimension of the current gray scale image. The classifier concatenates the adjusted characteristic pattern and the current gray scale image to calculate a class confidence. The classifier determines whether the class confidence is larger than a confidence threshold, and outputs a current classification result if the class confidence is larger than the confidence threshold. A storage device stories the current classification result.

Abstract: A protective structure is provided, which includes a porous layer and a surface layer disposed on the porous layer. The porous layer includes a first copolymer, a plurality of pores, and a plurality of first silica particles, wherein the first copolymer is polymerized from a first monomer composition. The first monomer composition includes N,N-dimethylacrylamide and N-vinylpyrrolidone. The surface layer includes a second copolymer, a plurality of fibers, and a plurality of second silica particles, wherein the second copolymer is polymerized from a second monomer composition. The second monomer composition includes N,N-dimethylacrylamide and N-vinylpyrrolidone.

Abstract: A protective material is provided, which includes a polymer polymerized from a monomer and a plurality of silica particles. The monomer includes acrylamide with a formula of wherein each R is independently H or C1-3 alkyl group, and R? is C2-5 alkylene group. The silica particles and the polymer have a weight ratio of 1.5:1 to 4:1.

Abstract: A protective material is provided, which includes a polymer polymerized from a monomer and a plurality of silica particles. The monomer includes acrylamide with a formula of wherein each R is independently H or C1-3 alkyl group, and R? is C2-5 alkylene group. The silica particles and the polymer have a weight ratio of 1.5:1 to 4:1.

Abstract: A shear thickening formulation and composite material employing the same are provided. The shear thickening formulation includes inorganic particles and polyethylene glycol. The inorganic particles and the polyethylene glycol have a weight ratio of 3 to 4. The inorganic particles can be silica, aluminum oxide, silicon carbide, nano diamond, or a combination thereof.

Abstract: A shear thickening formulation and composite material employing the same are provided. The shear thickening formulation includes inorganic particles and polyethylene glycol. The inorganic particles and the polyethylene glycol have a weight ratio of 3 to 4. The inorganic particles can be silica, aluminum oxide, silicon carbide, nano diamond, or a combination thereof.

Abstract: A alarm system for waste liquid discharge blockage for early warning when blockage results from waste liquid. In the blockage alarm system for waste liquid discharge according to the present invention, the discharge line connected to the outlet of waste liquid of the machine is arranged such that the discharge line is angled in the ranges of about 185 to 265 degrees to allow the waste liquid flow smoothly. The first pipe and the second pipe of the sensing line are arranged roughly under the connection of the discharge line and the lowest outlet, and located to make the angle between the first pipe and the discharge line of the sensing region in the range of about 95 to 175 degrees and the angle between the second pipe and the discharge line of the sensing region in the range of about 95 to 175 degrees when the discharge line of the sensing region is vertical. The flow back of the waste liquid into the sensing region can be inhibited. A sensor can be provided in the sensing line for detection of blockage.