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New Energy Materials Engineering Lab
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A lab that explores semiconductor crystal growth through simulation and AI.
AboutAbout the lab
Recreating the growth of next-generation semiconductors in virtual space
The lab studies next-generation semiconductors as functional materials that contribute to energy saving and the SDGs, aimed at applications such as high-efficiency power conversion devices and deep ultraviolet light sources.
Next-generation semiconductors are industrially produced by chemical vapor deposition (CVD), but the process is a complex system in which several elementary processes are intertwined.
The lab develops eXS2-CVD, a simulation technology that reproduces the entire CVD process in virtual space.
The lab also works on "process informatics," which uses AI to search for optimal growth conditions.
Key terms
- Chemical vapor deposition (CVD)
- A method of growing thin films by supplying source gases to a heated substrate and using gas-phase and surface reactions. It is widely used in industry
- Process informatics
- An approach that analyzes data from experiments, measurements, and computational science with AI to find optimal conditions for materials fabrication
- eXS2-CVD
- A simulation technology developed by this lab that reproduces the entire CVD process in virtual space
ResearchResearch themes
The lab aims to elucidate the crystal growth processes used to make next-generation semiconductors, using simulation and AI. It develops a simulator that reproduces the chemical vapor deposition process in virtual space, and conducts research that uses machine learning to find optimal growth conditions.
*Illustrative imageElucidating the Chemical Vapor Deposition Process
The lab develops and improves eXS2-CVD, a simulation technology that reproduces in virtual space the entire chemical vapor deposition process used to fabricate next-generation semiconductors.
*Illustrative imageExploring Unexplored Materials with AI
In collaboration with the Materials Informatics field and the Interdisciplinary Integration Office, the lab is opening up a new area of "process informatics" through machine learning that makes use of eXS2-CVD. AI is used to search for optimal growth conditions.
MembersFaculty
The members of the New Energy Materials Engineering Lab consist of faculty members, academic researchers, visiting researchers, technical staff, and doctoral and master's students.As of 2026
- ProfessorYoshihiro Kangawa
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Argonne's Tribology Lab Plasma-Assisted Chemical-Vapor Deposition” Argonne National Laboratory / CC BY-SA 2.0 (cropped)
- Elucidating the Chemical Vapor Deposition Process: “CVD Reaction Chamber - GPN” NASA/GRC/Martin Brown and Quentin Schwinn OD CVD Reaction Chamber / Public domain (cropped)
- Exploring Unexplored Materials with AI: “Data flock (digits) by Philipp Schmitt” Philipp Schmitt / CC BY-SA 4.0 (cropped)
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