U.S. Patent Granted to Mitsubishi Chemical, National Institute for Materials Science, Nichia, and Citizen Electronics for LEDs Using Nitride-type Red Phosphor
2018.09.27
Mitsubishi Chemical Corporation
National Institute for Materials Science
Nichia Corporation
Citizen Electronics Co., Ltd.
Mitsubishi Chemical Corporation today announced that an LED-related patent shared by MCC, National Institute for Materials Science, Nichia Corporation, and Citizen Electronics Co., Ltd. has been granted in the U.S. The patent relates to the LEDs using a nitride-type red phosphor.
Abstract
MCC, NIMS, Nichia, and Citizen in 2015 concluded a cross-licensing agreement for the red phosphor, which is broadly used in LED applications. Among the patents targeted by the cross-licensing agreement, the U.S. patent, the basic patent for the red phosphor is shared by the four companies, and now another basic patent related to LEDs using this type of red phosphor has been granted in the U.S.
MCC, Nichia, NIMS, and Citizen shall not overlook the group of patents related to the red phosphor and LEDs using this phosphor from infringement by other companies and take appropriate measures in case of unauthorized use of this patent group.
Note 1 : A phosphor with a basic composition of (Sr, Ca) AlSiN3: Eu, etc. It is a red phosphor, which is most broadly used for white LEDs thanks to its high brightness and reliability. This phosphor is generally called CASN or SCASN phosphor, or 1113 phosphor.
Contacts
-
Public Relations and Investor Relations Office
Mitsubishi Chemical Holdings Corporation
Tel: [+81] (0)3-6748-7140 -
Public Relations
Nichia Corporation
Tel: [+81] (0)884-22-2311 -
Public Relations Office
National Institute for Materials Science
Tel: [+81] (0)29-859-2026
Fax: [+81] (0)29-859-2017
E-Mail: pressrelease=ml.nims.go.jp
(Please change "=" to "@")
Recent Press Release
-
Simultaneous Imaging of Intracellular DNA and RNA Using Harmless Light
2025.10.27
-
Successful Visualization of the Odor Discrimination Process in an AI-Assisted Olfactory Sensor
2025.09.11
-
High-Precision Analysis of Microstructures in 2D Materials Using Electron Microscopy and Machine Learning
2025.08.25