Abstract: A field effect transistor includes a substrate having a transistor forming region thereon; an insulating layer on the substrate; a first graphene layer on the insulating layer within the transistor forming region; an etch stop layer on the first graphene layer within the transistor forming region; a first inter-layer dielectric layer on the etch stop layer; a gate trench recessed into the first inter-layer dielectric layer and the etch stop layer within the transistor forming region; a second graphene layer on interior surface of the gate trench; a gate dielectric layer on the second graphene layer and on the first inter-layer dielectric layer; and a gate electrode on the gate dielectric layer within the gate trench.

Abstract: The present invention relates to an ultrasound image enhancement processing system and method thereof. The ultrasound image enhancement processing system includes at least one first ultrasound device and at least one server apparatus. The server apparatus receives a first ultrasound original image file, processes the first ultrasound original image file through a speckle reduction algorithm to generate a first processed image file, and performs a deep learning training on the first ultrasound original image file and the first processed image file to generate a first neural network model. The first neural network learning module is used to output a first speckle reduction enhancement image file. The image content of the first speckle reduction enhancement image file is approximating to the image content of the first processed image file.

Abstract: The present invention relates to a license plate identification system and method thereof. The license plate identification system comprises at least a server apparatus storing at least one license plate picture file to be identified and a plurality of license plate sample picture files. Each of the license plate sample picture files has a double-row of character. Through deep learning training, the system generates a neural network model, inputs the license plate picture file to be identified into the neural network model for outputting analysis result information, and decodes the analysis result information through a decoding algorithm to obtain an identification license plate character content.

Abstract: An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

Abstract: An electronic device and a method for selecting a feature of an electrocardiogram (ECG) are provided. The method includes: obtaining the ECG; performing a first pre-processing on the ECG to generate a first ECG; marking multiple extreme points corresponding to at least one type of wave on the first ECG; calculating a first feature value corresponding to a first feature according to the multiple extreme points of the at least one type of wave, generating a first performance index corresponding to a machine learning model according to the first feature value, and determining whether to select the first feature according to the first performance index; and outputting the first feature in response to selecting the first feature.

Abstract: An SONOS memory cell includes a silicon substrate. A tunnel silicon oxide layer, a silicon nitride layer and a silicon oxide layer are disposed from bottom to top on the silicon substrate. The silicon oxide layer includes two first silicon oxide layers and a second silicon oxide layer. A thickness of the silicon oxide layer is smaller than a thickness of each of the first silicon oxide layers. A control gate covers and contacts the silicon oxide layer. A first source/drain doping region and a second source/drain doping region are respectively disposed at two sides of the control gate. The silicon oxide layer has a cross section. The second silicon oxide layer is sandwiched between the two first silicon oxide layers on the cross section.

Abstract: A method for forming a thin film resistor with improved thermal stability is disclosed. A substrate having thereon a first dielectric layer is provided. A resistive material layer is deposited on the first dielectric layer. A capping layer is deposited on the resistive material layer. The resistive material layer is then subjected to a thermal treatment at a pre-selected temperature higher than 350 degrees Celsius in a hydrogen or deuterium atmosphere. The capping layer and the resistive material layer are patterned to form a thin film resistor on the first dielectric layer.

Abstract: A school admission prediction system and method are provided. The system includes a user interface for receiving personal data from an applicant, including academic and activity data. A data acquisition module connected to the user interface acquires this personal data. A data preprocessing module connected to the data acquisition module preprocesses the academic and activity data. An attribute selection module connected to the data preprocessing module extracts multiple attributes from the preprocessed data. A machine learning model generates an evaluation report based on the extracted attributes. This report includes a prediction of whether the applicant will be admitted to the school. The system also includes a loss calculation module for evaluating the performance of the machine learning model and optimizing its parameters based on the evaluation results. The method and system provide a reliable and efficient way to predict school admissions, helping applicants to better prepare their applications.

Abstract: A display apparatus is adapted to display on at least one of a first display surface and a second display surface, and includes a display panel, an optical path selection module, and at least one reflective element. The display panel is provided with the first display surface, and is adapted to emit an image beam. The optical path selection module is disposed on a transmission path of the image beam. The image beam transmitted along a first optical path after passing through the optical path selection module is adapted to display a first image on the first display surface. The image beam transmitted along a second optical path after passing through the optical path selection module is adapted to display a second image on the second display surface. The first image is different from the second image. The at least one reflective element is disposed on the second optical path.

Abstract: A broadband linear polarization antenna structure, including a reference conductive layer, a first patch antenna, a second patch antenna, and a feeding portion, is provided. The reference conductive layer includes through holes. A first short pin is connected between the reference conductive layer and the first patch antenna, and a second short pin is connected between the first patch antenna and the second patch antenna. Each feeding portion penetrates the reference conductive layer through the through hole and is coupled to the first patch antenna.

Abstract: Disclosed are a semiconductor sensing chip and a microfluidic sensing system. The microfluidics sensing system includes a first inlet and a second inlet, a fluidic structure, and a semiconductor sensing chip. The first inlet and the second inlet are respectively configured for injection of a sample and a reagent. The fluidic structure is coupled to the first inlet and the second inlet. The fluidic structure is configured to mix the sample and the reagent to generate a biofluid under test. The semiconductor sensing chip is disposed at the end of the fluidic structure and configured to sense the biofluidic under test and generate a concentration sensing result corresponding to the sample.

Abstract: A training method and training system for neural network model. The training method includes: (a) receiving image data; (b) preforming first-tier calculation including first convolution and first non-linear calculation; (c) performing combination computation; (d) performing second-tier calculation based on output of the combination computation when it is determined to execute, and it includes second convolution and second non-linear calculation; and (e) when the second-tier calculation is determined not to execute, classifying the output of the combination computation. Step (c) includes: (c1) grouping output of the first-tier calculation; (c2) performing linear and non-linear computation respectively on different groups of the output of the first-tier calculation; and (c3) performing consolidate computation based on output of the linear and the non-linear computation.

Abstract: A communication method in a virtual environment includes initiating, by a first user, a virtual classroom through an education Metaverse application program of a first user device; attending, by at least a second user, the virtual classroom through an education Metaverse application program of a second user device; activating, by the first user, a class teaching mode, such that the first user and at least the second user login a cloud streaming system through corresponding education Metaverse application program of the first user device and the second user device to represent a first Avatar and a second Avatar for communication in the virtual classroom; wherein the virtual classroom is configured to render a spatial audio in a three-dimensional (3D) space.

Abstract: An integrated chip including a substrate and a transistor device along the substrate. A plurality of conductive interconnects are over the transistor device. A first under-bump metal (UBM) layer is over the conductive interconnects. A first metal bump is directly over the first UBM layer. A metal-insulator-metal (MIM) capacitor array is over the transistor device and under the first UBM layer. The MIM capacitor array includes a first MIM capacitor and a second MIM capacitor coupled in parallel and disposed directly under the first UBM layer.

Abstract: A slot antenna structure includes a metal trough and two metal plates. The metal trough includes a metal bottom plate, two metal sidewalls erected on two opposite long sides of the metal bottom plate respectively, two metal closing walls erected on two opposite short sides of the metal bottom plate respectively, and an opening formed and surrounded by the metal sidewalls and the metal closing walls. The metal plates are provided on the opening. A slot is formed between the metal plates and extends in a straight line to separate the metal plates. An electrical unit is provided on the metal plates and astride the slot. The metal plates and the metal trough form a resonance chamber in communication with the slot. The depth from the opening to the inner bottom side of the metal trough is generally one eighth of the wavelength of an input signal.

Abstract: A process for continuous production of z-threaded fiber reinforced polymer composites. The process includes providing a pre-formed fiber fabric including a plurality of fibers and a pre-formed, solidified film having a film thickness dimension, wherein the film includes a heat-meltable base matrix material in combination with a plurality of z-aligned nanofibers disposed within the base matrix material. The film and fiber fabric are advanced in layered relation through a constricting matrix transfer station, wherein the fiber fabric is heated to a temperature at or above the melting point of the base matrix material and the base matrix material progressively melts at an interface with the fabric face and flows with the nanofibers into the fiber fabric in the fabric thickness dimension as the film and fiber fabric move through the matrix transfer station.