Patents by Inventor Tzu-Cheng Lee

Tzu-Cheng Lee has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11031498
    Abstract: A semiconductor structure includes a substrate, first fins extending from the substrate with a first fin pitch, and second fins extending from the substrate with a second fin pitch that is smaller than the first fin pitch. The semiconductor structure also includes first gate structures engaging the first fins, second gate structures engaging the second fins, first epitaxial semiconductor features adjacent the first gate structures, and second epitaxial semiconductor features adjacent the second gate structures. The first epitaxial semiconductor features are partially embedded in the first fins at a first depth, and the second epitaxial semiconductor features are partially embedded in the second fins at a second depth that is smaller than the first depth.
    Type: Grant
    Filed: April 20, 2020
    Date of Patent: June 8, 2021
    Assignee: TAIWAN SEMICONDUCTOR MANUFACTURING CO., LTD.
    Inventors: Wei-Yang Lee, Tzu-Hsiang Hsu, Ting-Yeh Chen, Feng-Cheng Yang
  • Patent number: 11004753
    Abstract: A display device includes a substrate, a light-emitting element, and a transistor. The substrate has a top surface. The light-emitting element is disposed on the substrate. The transistor is disposed on the substrate, and includes a drain electrode, a gate electrode, and a semiconductor layer. The drain electrode is electrically connected to the light-emitting element. The semiconductor layer includes an overlapping portion overlapped with the gate electrode. The light-emitting element does not overlap with the overlapping portion along a direction perpendicular to the top surface of the substrate.
    Type: Grant
    Filed: January 10, 2019
    Date of Patent: May 11, 2021
    Assignee: INNOLUX CORPORATION
    Inventors: Tung-Kai Liu, Tsau-Hua Hsieh, Wei-Cheng Chu, Chun-Hsien Lin, Chandra Lius, Ting-Kai Hung, Kuan-Feng Lee, Ming-Chang Lin, Tzu-Min Yan, Hui-Chieh Wang
  • Patent number: 10987398
    Abstract: The present disclosure relates to a herbal compound extract to moderate diabetes with liver necrosis and fibrosis and applications thereof wherein a herbal compound consists of 10 to 20 units rhizome of Dendrobium nobile Lindl, 6 to 12 units fruiting body of Antrodia camphorata, 12 to 20 units root of Panax ginseng C. A. Mey, 10 to 30 units root of Rehmannia glutinosa Libosch, 15 to 30 units rhizome of Salvia miltiorrhiza Bge., 6 to 12 units all of Pheretima asperfillm (E. Perrier), 10 to 30 units root of Pueraria mirifica, 8 to 15 units fruit of Schisandra chinensis (Turcz.) Baill and 6 to 8 units rhizome of Glycyrrhiza uralensis Fisch and the herbal compound extract is able to moderate symptoms comprising hyperglycemia, hyperlipidemia, abnormal liver function about liver necrosis and fibrosis due to the diabetes.
    Type: Grant
    Filed: May 21, 2018
    Date of Patent: April 27, 2021
    Assignee: OMICS LIFESCIENCE CO., LTD
    Inventors: Tzu-Chih Hsiao, Su-Cheng Lee, Chia-Fu Hsiao, Yen-Yu Hsiao
  • Patent number: 10937206
    Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.
    Type: Grant
    Filed: January 18, 2019
    Date of Patent: March 2, 2021
    Assignee: Canon Medical Systems Corporation
    Inventors: Yujie Lu, Zhou Yu, Jian Zhou, Tzu-Cheng Lee, Richard Thompson
  • Publication number: 20210012541
    Abstract: A method and apparatus is provided to improve the image quality of images generated by analytical reconstruction of a computed tomography (CT) image. This improved image quality results from a deep learning (DL) network that is used to filter a sinogram before back projection but after the sinogram has been filtered using a ramp filter or other reconstruction kernel.
    Type: Application
    Filed: July 11, 2019
    Publication date: January 14, 2021
    Applicant: Canon Medical Systems Corporation
    Inventors: Tzu-Cheng LEE, Jian ZHOU, Zhou YU
  • Publication number: 20210007702
    Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).
    Type: Application
    Filed: July 12, 2019
    Publication date: January 14, 2021
    Applicant: Canon Medical Systems Corporation
    Inventors: Tzu-Cheng LEE, Jian ZHOU, Zhou YU
  • Publication number: 20200311490
    Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.
    Type: Application
    Filed: April 1, 2019
    Publication date: October 1, 2020
    Applicant: CANON MEDICAL SYSTEMS CORPORATION
    Inventors: Tzu-Cheng LEE, Jian Zhou, Zhou Yu
  • Publication number: 20200234471
    Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.
    Type: Application
    Filed: January 18, 2019
    Publication date: July 23, 2020
    Applicant: Canon Medical Systems Corporation
    Inventors: Yujie Lu, Zhou Yu, Jian Zhou, Tzu-Cheng Lee, Richard Thompson