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    TRANSPARENT CONDUCTOR AND ELECTRONIC DEVICE
    3.
    发明申请
    TRANSPARENT CONDUCTOR AND ELECTRONIC DEVICE 有权
    透明导体和电子器件

    公开(公告)号：US20160293880A1

    公开(公告)日：2016-10-06

    申请号：US14777606

    申请日：2014-03-18

    Applicant: KONICA MINOLTA, INC.

    Inventor： Takeshi HAKII ,  Toshiyuki KINOSHITA ,  Shigeru KOJIMA ,  Kazuhiro YOSHIDA

    IPC: H01L51/52 ,  B32B15/04

    CPC classification number: H01L51/5215 ,  B32B15/04 ,  B32B2307/202 ,  B32B2307/412 ,  B32B2457/00 ,  C22C5/04

    Abstract: A transparent conductor which includes: a conductive layer that is formed of a metal material having a thickness of 15 nm or less and a platinum group element-containing layer including at least one of Pt and Pd, wherein, when an optical admittance at an interface on a side of the admittance-adjusting layer of the conductive layer at a wavelength of 570 nm is expressed as Y1=x1+iy1 and an optical admittance at an interface on a side opposite to the admittance-adjusting layer of the conductive layer at a wavelength of 570 nm is expressed as Y2=x2+iy2, at least one of x1 and x2 is 1.6 or more.

    Abstract translation: 一种透明导体，其包括：由厚度为15nm以下的金属材料形成的导电层和包含Pt和Pd中的至少一种的含铂族元素的层，其中，当界面处的光学导纳 在导电层的纳米调节层的波长为570nm的一侧表示为Y1 = x1 + iy1，并且在导电层的导纳调整层的与导电层的导纳调整层相反的一侧的界面处的光导通 570nm的波长表示为Y2 = x2 + iy2，x1和x2中的至少一个为1.6以上。

    LEARNING DEVICE, READER, AND LEARNING PROGRAM
    9.
    发明公开
    LEARNING DEVICE, READER, AND LEARNING PROGRAM 审中-公开

    公开(公告)号：US20230196039A1

    公开(公告)日：2023-06-22

    申请号：US17998875

    申请日：2021-04-02

    Applicant: Konica Minolta, Inc.

    Inventor： Ippei ENOKIDA ,  Takumi ISHIWATA ,  Takeshi HAKII

    IPC: G06K7/08 ,  G06K19/067

    CPC classification number: G06K7/08 ,  G06K19/0672

    Abstract: This learning device is provided with: a simulation execution unit that, by using electromagnetic field analysis simulation, determines a reflected wave spectrum obtained when electromagnetic waves are emitted from a reader to an identification target; and a machine learning unit that, by using training data in which the reflected wave spectrum calculated by the simulation execution unit and an attribute thereof are defined as a set, performs a training process on a learning model by machine learning. The simulation execution unit generates a plurality of the reflected wave spectra belonging to the same attribute by variously changing various parameters related to the identification target from reference parameters. The machine learning unit performs a training process on the learning model by machine learning by using, as training data, the plurality of reflected wave spectra obtained for each attribute.

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