- Research Topics 2023 - 2024
Research Topics 2023 - 2024
Applications for the 2023 - 2024 Programme are now closed.
Topic 1: System Security for Industrial Control Systems
Recently, industrial control systems (ICS) for critical infrastructure, such as power plants and water distribution control and management systems, have been digitalized. While the digitalization achieves sophistication of data driven O&M and optimization of the control algorithm, there are emerging cyber threats against industrial control system as well as vulnerability exploitation and sensitive data exfiltration. Many sophisticated attacks exploit zero-day vulnerabilities. Since exploitation of a vulnerability in such systems may cause severe consequences in the physical world, it is a heavy responsibility of system/device vendor including Toshiba to mitigate vulnerabilities and attack surface. While the mitigation can be done through vulnerability testing and exploit synthesis using binary program analysis and cyber-attacks such as penetration test, there are remaining challenges specific to ICS such as timing constraint consideration, non-standard instruction set and byte code interpreters. Furthermore, data utilization of the infrastructure control field also follows increasingly by progress of AI technology etc., and we expect growing needs for the data protection technology and security protocols that ensure both analyzability and security (to leak with a disclosure risk of sensitive information).
Expected Research Proposals
Security Verification for Software Implementation in industrial control systems and devices, in detail, finding vulnerabilities, composing attacks and/or synthesizing patches technology for binary programs and systems. There have already been many studies in this field, so it is important to extend existing techniques with new idea to apply them to industrial control system and devices.
Data protection technology and data protection protocols suitable for industrial control system, such as, on the premise of multiple systems, a secure search of various O&M data and a secure fault prediction/detection technique of the system, furthermore industrial data analysis without exposing sensitive information, such as searchable encryption.
 T. Avgerinos, et al. AEG: Automatic Exploit Generation. In Proc. of NDSS, 2011.
 P. Godefroid, et al. Automated Whitebox Fuzz Testing. In Proc. of NDSS, 2008.
 Nakanishi, et al. Automated Attack Path Planning and Validation (A2P2V). BlackHat USA Arsenal, 2021
 Komano, et al., Toward Highly Secure Metering Data Management in the Smart Grid, https://www.imi.kyushu-u.ac.jp/wp-content/uploads/2022/07/mil_73.pdf , pp.113-120
 Start of Project to Verify Open Platform Aggregation Business, https://www.toshiba-energy.com/en/info/info2020_0608.htm
Topic 2: Super Accelerating Scientific Simulation for Physics-Based Real-Time Digital-Twin
Real-time digital-twin concepts as an inter-mediator taking care of the interaction among human, nature, robots and infrastructure systems would be to simulate the complex real world interactions as well as to run the real world in the cyber physical systems .
Digital-twin computing could well improve infrastructure resilience for various risks, and to control and optimize design, operation and maintenance in complex network systems.
Scientific simulations such as fluid mechanics, non-linear mechanics, multi-physics and multi-scale phenomena are implementations of theoretical models, they may run very slowly, even on modern computer hardware; for example, global climate models may take thousands of CPU-hours.
It is necessary to highly accelerate simulations - computer implementations of mathematical models - to test out new concepts and ideas, and explore potential behaviors of infrastructure systems and natural systems.
Toshiba has owned the data, model and knowhow of infrastructure systems, and has potential and environment to develop digital-twin computing technologies .
Expected Research Proposals
Physics-based real-time digital-twin for model predictive control by accelerating scientific simulation with machine learning method such as physics-informed neural networks, Hamiltonian networks, Galerkin projection and sparse identification, for computational fluid dynamics, continuum mechanics and nonlinear dynamics .
Accelerating scientific simulation utilizing quantum inspired technologies .
Making accurate prediction and control in complex network systems based high-dimensional data predictable method and control method .
Accelerating hybrid simulations of multi-agent simulation and physical simulation for a large number of risk scenarios in infrastructure systems .
Topic 3: Quantum Machine Learning
The number of physical qubits available for quantum calculation is continuously increasing in the recent years. Yet a perfect logical qubit with error correction functions has not been accomplished by any quantum computer providers. Fault Tolerant Quantum Computer (FTQC) with enough number of qubits is essential to attain the quantum algorithms which is proved to have quantum advantage. And it is predicted to be accomplished in 10 years or more later.
As the result of the previously mentioned above, the recent quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ) computer, are constructed by physical qubits that store the data for short periods of time and a set of state control gate without sufficient fidelity. Accompanied with number of qubit growth and the progress of quantum algorithm, not only the research of machine toward the FTQC, but also whether NISQ computers will take an important role of solving real world problems or not, becomes an attracting question for our society.
Toshiba is working for a sustainable future for the earth and its people by contributing to the realization of carbon neutrality and a circular economy. We are looking for quantum evolution in which quantum technologies drive the social value creation. Toshiba and Japanese partner companies together established the industrial alliance (Q-STAR) to create the quantum industries. Toshiba also becomes a member of the Quantum Innovation Initiative Consortium (QIIC) to conduct quantum computing research by obtaining superior access to IBM’s superconductor type quantum computers via membership to the IBM Quantum Network. We are eager to use this opportunity on quantum machine learning research that is expected to help us achieving our vision.
Expected Research Proposals
We are expecting research proposals related to quantum algorithms available for NISQ devices. In particular, it may be related to the following items:
Quantum machine learning algorithms by taking the advantage of the right to access the quantum computers.
Parametrized quantum circuits optimization methods to tackle the barren plateau problem, reduce its process and be robust to noise.
Effective techniques related to data encoding, ansatz, error mitigation and efficient quantum state measurement for raising up machine learning abilities.
 Toshiba Corporate Information: Our strategy | Corporate Information | Toshiba (global.toshiba)
 Toshiba News Release: Establishment of Quantum Strategic Industry Alliance for Revolution (Q-STAR) | News | Toshiba (global.toshiba)
 IBM News Release: IBM and the University of Tokyo Unveil the Quantum Innovation Initiative Consortium to Accelerate Japan's Quantum Research and Development Leadership - Jul 30, 2020
 Boston Consulting Group publication: The Path to Building Quantum Advantage | BCG
 arXiv: [2101.08448] Noisy intermediate-scale quantum (NISQ) algorithms (arxiv.org), 2021
Topic 4: Next-Generation Innovative Electronic Devices
Toshiba group aims to contribute to the achievement of carbon neutrality and a circular economy through digitization under our basic commitment of "Committed to People, Committed to the Future." and is presently developing new technologies in CPS (Cyber Physical System) fields. We are working on the R&D of power semiconductor device / module, sensing device / module, photonics-electronics convergence device / module and information storage device as technologies that support the CPS, and in the future, we will promote further high functionality and high performance by integrating with IoT (Internet of Things) and AI (Artificial Intelligence) technologies. Specifically, we aim to create the following innovative next-generation device and module technologies.
Next-generation high-performance Si, SiC, and GaN power semiconductor device technologies [2,3,4] and innovative highly integrated power module and high power density power converter technologies .
Innovative sensing and photonics-electronics convergence device / module technologies based on semiconductor technology such as MEMS [6,7] and silicon photonics [8,9].
Next-generation information storage device and spintronic device / module technologies by integrating spintronic technology with AI and machine learning [10,11].
Expected Research Proposals
The following are examples of research themes that we expect as research proposals.
Research and development of innovative power semiconductor devices including Si, SiC and GaN that realize power saving.
Research and development of highly integrated power module and high power density power converter using innovative Si, SiC, and GaN power semiconductor devices.
Application of AI technologies for precise analysis and design of power semiconductor devices and modules.
Research on device control technologies for inertial sensors and sensor fusion technologies for position estimation.
Research on highly sensitive, low power consumption, and highly selective gas sensor technologies utilizing AI and machine learning.
Research on photonics-electronics convergence devices/modules technologies that integrates electronic and optical devices based on silicon photonics technologies.
Research on spintronic device design and data processing application technology for ultra-large capacity HDDs.
Research on device analysis and CPS module technology for innovative and ultra-sensitive spintronic sensors.
Not limited to examples described here, we look forward to receiving research proposals for future technologies related to innovative next-generation devices and module technologies in CPS fields.
 T. Sakano, et.al, “Three-level Gate Drive Technique for Enhancing Switching Loss Reduction in Triple-Gate IGBTs”, in Proc of the 34th Int. Symp. on Power Semicond. Devices & ICs, p.117(2022)
 T. Ohashi, et.al, “Improved Clamping Capability of Parasitic Body Diode Utilizing New Equivalent Circuit Model of SBD-embedded SiC MOSFET”, in Proc of the 33rd Int. Symp. On Power Semicond. Devices & Ics, p.79(2021)
 Y. Kajiwara, et.al, “Suppression of current collapse by Access-region Carrier Enhancement technique in GaN-MOSFETs”, in Proc of the 34th Int. Symp. on Power Semicond. Devices & ICs, p.317(2022)
 K. Arita, et.al, “99%, 15W/cm3 capacitively coupled modular DCPET for low-voltage power supply system”, in Proc of Int. Power Electronics Conference 2022 ECCE AISA, p.2628(2022)
 Y. Hayashi, et.al, “Smoldering Fire Detection Using Low-Power Capacitive MEMS Hydrogen Sensor for Future Fire Alarm”, in Proc. of the 21st International Conference on Solid-State Sensors, Actuators and Microsystems, p.267(2021)
 F. Miyazaki, et.al, “A 0.1 Deg/H Module-Level Silicon MEMS Rate Integrating Gyroscope Using Virtually Rotated Donut-mass Structure and Demonstration of the Earth’s Rotation Detection”, in Proc. of the 21st International Conference on Solid-State Sensors, Actuators and Microsystems, p.402(2021)
 K. Ohira, et. al, “On-chip optical interconnection by using integrated III-V laser diode and photodetector with silicon waveguide”, Optics Express, Vol.18, p.15440 (2010)
 J. W. Silverstone, K. Ohira, et. al, “On-chip quantum interference between silicon photon-pair sources”, Nature Photonics?Vol.8, p.104 (2014)
 M. Takagishi, et.al, “Design Concept of MAS Effect Dominant MAMR Head and Numerical Study”, in IEEE Transaction on Magnetics Vol.57, Iss.3, 9208742(2021)
 S. Shirotori, et.al, “Symmetric response magnetoresistance sensor with low 1/f noise by using an anti-phase AC modulation bridge”, in IEEE Transaction on Magnetics Vol.57, Iss.2, Part1, 4000305(2021)