Showa Denko (SDK) (TOKYO: 4004) proposed its “Project to Develop SiC Wafers Technology for Next-generation Green Power Semiconductors*1” (hereinafter “the Project”) to New Energy and Industrial Technology Development Organization (NEDO) as a candidate for “Projects to Develop Wafers Technology for Next-generation Power Semiconductors” which was set as a research and development target of “Next-generation Digital Infrastructure Construction*2” in “Green Innovation Fund Projects” (hereinafter GI Fund Projects). And the Project has been selected for GI Fund Projects by NEDO.
In October 2020, the Japanese Government declared that it aims to achieve carbon neutrality by 2050. Aiming to significantly accelerate efforts toward structural changes in the energy and industrial sectors and undertake bold investment for innovation, which are necessary for achievement of the above national target, Ministry of Energy, Trade and Industry (METI) decided in October 2020 to develop a Green Innovation Fund at the level of 2 trillion yen as part of the NEDO. The plan of GI Fund Projects, which are based on the specific goals shared by public and private sectors, is to continuously support companies and other organizations, which show their commitment to challenge such ambitious goals as their business issues ranging from research and development (R&D) to demonstrations to social implementation of the outcomes.
SDK’s business to manufacture SiC epitaxial wafers for power semiconductors (hereinafter SiC epi-wafers) has conducted business transactions with leading power semiconductor manufacturers inside and outside Japan, and has the global-top share in the SiC epi-wafers market*3. In the Project, SDK plans to make the most of its resources including intelligent property portfolio and development know-how, develop SiC epi-wafers with a diameter of 8 inches, and reduce density of deficiencies by one digit or more, thereby reducing production cost of next-generation power semiconductors. This time, NEDO highly appreciated SDK’s aggressive proposal and its SiC epi-wafers business’s good performance. As a result, NEDO selected SDK’s proposal for GI Fund Projects. The Project’s implementation period will be 9 years, from fiscal 2022 to fiscal 2030. In the Project, SDK will develop technology to accelerate growth rate of SiC bulk single crystal in cooperation with the National Institute of Advanced Industrial Science and Technology (AIST).
The Showa Denko Group aims to be a “Co-Creative Chemical Company” and contribute to the sustainable development of global society. Under this vision, SDK positions its operation to produce SiC epitaxial wafers, which contributes to efficient use of energy, as a next-generation business, and will allocate much of our business resources. The Group will continue contributing to the spread of SiC power semiconductors by maintaining “Best in Class” as its motto and continuing provision of high-performance and highly-reliable products.
*1. Next-generation green power semiconductors are power semiconductors used in xEVs, power equipment for renewable energies, power modules for servers, etc., made from next-generation materials including SiC.
*2. URL for Projects to Next-generation Digital Infrastructure Construction is https://green-innovation.nedo.go.jp/en/project/building-next-generation-digital-infrastructure/
*3. SDK has the global-top share in the market as an independent supplier of SiC epitaxial wafers.
This news originally published on www.sdk.co.jp
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Showa Denko K.K. (SDK) (TOKYO: 4004) (President and CEO: Hidehito Takahashi) has introduced MLOps* (Machine Learning Operations) for efficient management of machine learning models deployed into Artificial Intelligence (AI) systems for materials design ahead of its competitors. Machine learning models can predict material properties based on formulations and manufacturing-process conditions of materials. This time, we automated input of the latest data into computers that develop machine learning models and data processing in those computers. This automation has reduced the time required to build and operate machine learning models from five days to one day per month. In addition, the introduction of MLOps enabled us to accelerate materials development by predicting material properties based on the latest data.
SDK utilizes AI systems for efficient materials development, such as exploring the optimal material formulation. Machine learning models deployed into the AI systems predict material properties from formulations or suggest formulations that improve material properties. The machine learning process for managing the AI systems includes inputting the latest data, data processing, and continuous training of machine learning models. Previously, data scientists had to input and process the latest data for themselves. These steps accounted for about 80% of the time required for the entire machine learning process. In addition, machine learning models deployed into the AI systems are built specifically for each material. Therefore, before introducing MLOps, the development of machine learning models required a lot of time and effort due to the necessary work specialized for each material.
Aiming to address these issues caused by applying AI systems to the development of numerous materials in the Company and operating machine learning models efficiently, we have installed programs to automate the input of the latest data and data processing into our AI systems. Moreover, we have introduced technologies that enable data scientists responsible for building machine learning models and software engineers responsible for building AI systems to develop systems collaboratively even if there are differences in operating systems and programming languages they use. By introducing MLOps ahead of our competitors to manage machine learning models efficiently, we can reduce the time required to develop machine learning models and their operation, improve prediction accuracy, and stably operate dozens of AI systems. As a result, now we can propose ideal materials to our customers promptly.
The Showa Denko Group will apply the fruits of basic research in AI and computational science to materials development and quickly provide solutions that solve our customers’ problems, thereby contributing to the development of a sustainable society.
Machine learning process from model development to operation
* MLOps: The method and philosophy for integrating the development and operation of machine learning models. MLOps include continuous training of machine learning models, automating the machine learning process, and establishing tools and operational rules for collaborative development between data scientists and software engineers.
This news originally published on www.sdk.co.jp