[ Professor ] Shigeo Sato
[ Associate Professor ] Masao Sakuraba
Group IV Quantum Heterointegration Web Site
[ Associate Professor ] Hideaki Yamamoto
Nano-Integration Neurocomputing Systems
[ Specially Appointed Assistant Professor ] Kwan-Soo Kim
[ Research Fellow ] Satoshi Moriya
In this laboratory, we focus on non-von Neumann computing such as brain computing and quantum computing, and study their hardware technology. We conduct research on various topics including device, process, circuit, algorithm, and neuroscience, and build revolutionary new computer technology by integrating our findings.
Nano-Integration Devices (Prof. Sato)
- Brain computing hardware.
- Intelligent quantum hardware.
- Brainmorphic visual information processing system.
Improvement in hardware efficiency and reduction of power consumption are important subjects in order to further promote social implementation of AI technology. We build brain computing hardware technology through developments of neuromorphic devices and dedicated LSIs, which solve these issues, and also AI systems composed of them.
Group IV Quantum Heterointegration(Assoc. Prof. Sakuraba)
- Low-damage plasma CVD process without substrate heating for epitaxial growth of highly strained group IV semiconductors
- Large-scale integration process of group IV semiconductor quantum heterostructures
- Fabrication of high-performance nanodevices utilizing group IV semiconductor quantum heterostructures
By utilizing plasma induced reaction and so on, heterostructure formation in a ultrathin region is investigated to explore novel electronic properties. Moreover, fundamental technology for integration of the quantum heterostructure onto Si LSIs is aimed.
Nano-Integration Neurocomputing Systems (Assoc. Prof. Yamamoto)
- Bioengineering technologies for manipulating neuronal network functions
- Computational modeling of neuronal networks
- Information processing in biological neuronal networks
Semiconductor microfabrication technologies can be used to manipulate living neurons and reconstruct well-defined neuronal networks that help bridge in vivo and in silico studies in neuroscience. Taking advantage of this experimental paradigm, we aim to better understand and build models on how the population activity of biological neurons realizes information processing in the brain.