Abstract: Embodiments described herein relate to a method for patterning a doping layer, such as a lanthanum containing layer, used to dope a high-k dielectric layer in a gate stack of a FinFET device for threshold voltage tuning. A blocking layer may be formed between the doping layer and a hard mask layer used to pattern the doping layer. In an embodiment, the blocking layer may include or be aluminum oxide (AlOx). The blocking layer can prevent elements from the hard mask layer from diffusing into the doping layer, and thus, can improve reliability of the devices formed. The blocking layer can also improve a patterning process by reducing patterning induced defects.

Abstract: An image sensor with an absorption enhancement semiconductor layer is provided. In some embodiments, the image sensor comprises a front-side semiconductor layer, an absorption enhancement semiconductor layer, and a back-side semiconductor layer that are stacked. The absorption enhancement semiconductor layer is stacked between the front-side and back-side semiconductor layers. The absorption enhancement semiconductor layer has an energy bandgap less than that of the front-side semiconductor layer. Further, the image sensor comprises a plurality of protrusions and a photodetector. The protrusions are defined by the back-side semiconductor layer, and the photodetector is defined by the front-side semiconductor layer, the absorption enhancement semiconductor layer, and the back-side semiconductor layer.

Abstract: An image sensor with an absorption enhancement semiconductor layer is provided. In some embodiments, the image sensor comprises a front-side semiconductor layer, an absorption enhancement semiconductor layer, and a back-side semiconductor layer that are stacked. The absorption enhancement semiconductor layer is stacked between the front-side and back-side semiconductor layers. The absorption enhancement semiconductor layer has an energy bandgap less than that of the front-side semiconductor layer. Further, the image sensor comprises a plurality of protrusions and a photodetector. The protrusions are defined by the back-side semiconductor layer, and the photodetector is defined by the front-side semiconductor layer, the absorption enhancement semiconductor layer, and the back-side semiconductor layer.

Abstract: A method for skin care and/or maintenance including applying or administering a preparation to a subject is provided. The preparation includes a biomedical composition, and the biomedical composition includes an effective amount of micelle, wherein the micelle includes a hyaluronic acid and/or a derivative thereof and a modified histidine. The modified histidine is grafted to at least one primary hydroxyl group of the hyaluronic acid and/or the derivative thereof, and a graft ratio of the modified histidine is 1-100%. Moreover, the hyaluronic acid and/or the derivative thereof and the modified histidine form the micelle on a weight percentage of 0.2-300:1.

Abstract: An image scanning method for a metallic surface and an image scanning system thereof are provided. The method includes sequentially moving one of a plurality of areas on a metallic surface of an object to a detection position, providing far infrared light by a light source component facing the detection position, wherein a light wavelength of the far infrared light is associated with the object, the far infrared light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by a photosensitive element according to the far infrared light, wherein the photosensitive element faces the detection position and a photosensitive axis of the photosensitive element is parallel to the normal line.

Abstract: An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image by a photosensitive element according to test light, determining whether a setting parameter of the photosensitive element is normal by a processor according to the test image, generating a warning signal if the setting parameter is abnormal, performing a detection procedure if the setting parameter is normal, sequentially moving one of a plurality of areas on a surface of an object to the detection position in the detection procedure, providing detection light by a light source component in the detection procedure to illuminate the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by the photosensitive element according to the detection light in the detection procedure.

Abstract: An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

Abstract: An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

Abstract: An artificial neural network-based method for detecting a surface pattern of an object includes: receiving a plurality of images, wherein the plurality of images are obtained by capturing an image of an object based on light with different lighting directions and a light incident angle is less than or equal to 90 degrees; superimposing initial images of the object images; and performing deep learning by using the plurality of initial images to build a predictive model for identifying the surface pattern of the object. Accordingly, the speed of identifying a surface pattern of an object is increased, further improving the product yield of the object.

Abstract: An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes sequentially moving one of a plurality of areas on a surface of an object to a detection position, providing light by a light source component facing the detection position, the light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by a photosensitive element according to the light, wherein the photosensitive element faces the detection position and a photosensitive axis of the photosensitive element is parallel to the normal line.

Abstract: An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

Abstract: An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image according to test light, determining whether the test image is normal, generating a coincidence signal if the test image is normal, coinciding the object with the detection position according to the coincidence signal, sequentially moving one of a plurality of areas on a surface of the object to the detection position, providing detection light facing the detection position, the detection light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position according to the detection light after the object is coincided with the detection position.

Abstract: A method for regulating a position of an object includes detecting a plurality of first alignment structures of the object under rotation of the object, wherein a plurality of second alignment structures of the object sequentially face a photosensitive element during the rotation of the object, and when the plurality of first alignment structures have reached a first predetermined state, stopping the rotation of the object and performing an image capturing procedure of the object. The image capturing procedure includes: capturing a test image of the object, wherein the test image includes an image block presenting the second alignment structure currently facing the photosensitive element; detecting the position of the image block in the test image; when the image block is located in the middle of the test image, capturing a detection image of the object.

Abstract: An artificial neural network-based method for detecting a surface pattern of an object includes receiving a plurality of object images, dividing each object image into a plurality of image areas, designating at least one region of interest from the plurality of image areas of each of the object images, and performing deep learning with the at least one region of interest to build a predictive model for identifying a surface pattern of the object.

Abstract: An array substrate and display device are disclosed. The array substrate includes signal lines; IC connection lines; the IC connection lines include at least two IC connection line groups, the at least two IC connection line groups include a first IC connection line group and a second IC connection line group, the base substrate further includes a dummy area, the dummy area is provided with a floating pattern, the dummy area includes a separation area and two sub-dummy areas, an extending line of the signal line corresponding to a first IC connection line and an extending line of the signal line corresponding to a second IC connection line respectively fall into the separation area, at least in the separation area, the floating pattern only includes first floating lines substantially extending along the second direction.

Abstract: The present invention discloses a Lithium ion battery and an electrolyte thereof, the electrolyte comprising an organic solvent, a lithium salt and an additive. The additive comprises additive (A) cyclic fluoro carbonate, additive (B) cyclic phosphazene (B), and additive (C) cyclic sulfate. The additive B cyclic phosphazene has the following general structural formula: Compared with the prior art, the electrolyte of the present invention may form a stable CEI and SEI film on the surface of positive and negative electrodes, protect the interface between positive and negative electrodes, improve the acidic atmosphere of Lithium ion battery electrolyte, and reduce the damage effect of HF on the interface between positive and negative electrodes, thereby improving cycle life, high temperature storage performance, and safety performance of lithium-ion battery.

Abstract: An image sensor device is provided. The image sensor device includes a semiconductor substrate having a first side, a second side opposite to the first side, and at least one light-sensing region close to the first side. The image sensor device includes a dielectric feature covering the second side and extending into the semiconductor substrate. The dielectric feature in the semiconductor substrate surrounds the light-sensing region. The image sensor device includes a reflective layer in the dielectric feature in the semiconductor substrate, wherein a top portion of the reflective layer protrudes away from the second side, and a top surface of the reflective layer and a top surface of the insulating layer are substantially coplanar.

Abstract: A car and a neighboring vehicle lane departure warning system and method are provided. The system includes a cloud server and a plurality of cars connected to the cloud server. Each car includes a vehicle body and a neighboring vehicle lane departure warning apparatus disposed on the vehicle body. The cloud server outputs a reminder signal to the cars within a specific distance of the neighboring vehicle after receiving a neighboring vehicle lane departure message, so that the drivers can be informed in time that the neighboring vehicle moves out of its lane, and can keep a proper distance from the neighboring vehicle in advance to prevent accidents.

Abstract: The invention relates to a universal semiconductor automatic high-speed serial signal testing method, comprising: a chip to be tested sending, to an impedance matching unit, a high-speed serial signal; then by means of a phase shift unit, sequentially transforming, according to a set fixed resolution, the phase of the high-speed serial signal, the magnitude of each offset phase being determined by a phase shift control signal outputted by a control unit and the resolution of the phase shift unit; after passing through the phase shift unit, the high-speed serial signal keeps channel impedance matching by means of the impedance matching unit; the signal entering an acquisition unit, and being acquired under the action of an acquisition control signal sent by the control unit; the control unit performing signal exchange with semiconductor automatic testing equipment (ATE); and the acquisition unit transmitting the acquired signal back to the universal semiconductor ATE for algorithm operation, and then the actua

Abstract: The present invention discloses a halo test method for an optical chip in an integrated circuit. A captured image array is processed as a circle by: dividing the array into circular patterns on the basis of the radius, reconstructing the circular patterns into a two-dimensional array according to coordinates, and then performing corresponding operations on the obtained array to obtain a desired value. By the halo test method for an optical chip in an integrated circuit provided in the present invention, without increasing any extra hardware cost and under the primary test conditions, the technical problem in the prior art that there is no well-developed method and algorithm for testing halo on a fingerprint on display (FOD) chip is solved.