The University of Tokyo · 물리·천문학
이 교수의 연구실은 스피nist릭스 기반의 뉴런 모방 컴퓨팅과 양자 어닐링 유사 기반 최적화 장치를 핵심으로 하며, 비선형 동역학, 잡음 환경에서의 안정성 확보, 그리고 생물의학 신호 처리 기술을 융합한 연구를 수행하고 있습니다. 특히 슈퍼파라미agnetic 터널 접합, 스핀 거품, 슈퍼파라미agnetic 재료를 활용한 스피nist릭스 레저보이어 컴퓨팅 및 고성능 생물자기 측정 기술 개발에 주력하고 있습니다. 또한, 복잡한 잡음 환경에서도 신뢰성 있게 동작하는 로직 연산 및 최적화 알고리즘 설계를 위한 비선형 동역학 기반의 새로운 컴퓨팅 아키텍처를 개발하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir comput
A method of magnetocardiography (MCG) measurement using an overdamped bistable model based stochastic resonance (SR) technique for advancing biomagnetic sensors is proposed. To determine the system parameters for SR, an evolutionary algorithm combined with the coherent detection (CD) method is applied. We acquire and process an animal MCG at room temperature, detected by a commercially available optically pumped magnetometer. A comparison with a lowpass filter and the traditional CD method verif
Superparamagnetic tunnel junctions (SMTJs) are spintronic nanodevices that can oscillate spontaneously under the influence of thermal noise. Studies have proposed that SMTJs can be modeled as magnetic neurons by subjecting them to electrical Gaussian white noise and can be subsequently synchronized under subthreshold driving. Considering the non-Gaussian background noise in industrial applications, this study investigated the influence of levy noise on SMTJ subthreshold synchronization. The resu
Logical stochastic resonance (LSR) is a paradigm to realize reconfigurable robust Boolean operations using specific nonlinearity in the presence of background noise. The stable-state number of the traditional LSR is less than four, which restricts its upper limit in the heavy noise floor. To further improve the noise robustness and output quality of LSR, we proposed quadstable nonlinearity based LSR system for the first time in this work. Using the parameters determined by the ant lion optimizer
Abstract Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this stu
Abstract Gain-dissipative Ising machines (GIMs) are dedicated devices that can rapidly solve combinatorial optimization problems. The noise intensity in traditional GIMs should be significantly smaller than its saturated fixed-point amplitude, indicating a lower noise margin. To overcome the existing limit, this work proposes an overdamped bistability-based GIM (OBGIM). Numerical test on uncoupled spin network show that the OBGIM has a different bifurcation dynamics from that of the traditional
Detecting QRS waves with high sensitivity and precision in noisy electrocardiogram (ECG) recordings is crucial for cardiovascular disease monitoring. A main challenge is the in-band noise contamination that overlaps with the ECG signal spectrum, which is difficult to completely eliminate using traditional filtering methods. Inspired by solid-state physics, we propose a 1-D lattice potential (OLP)-based algorithm to enhance robustness against in-band noise. The algorithm first bandpass filtered t
Logical stochastic resonance (LSR) system is a physical system capable of performing robust reconfigurable logical operations in the presence of background noise using specific nonlinearities. Traditional LSR systems are typically based on polynomial nonlinearities, which make them suitable for implementation by electronic components. However, there has been little research on LSR systems based on hyperbolic nonlinearities which have the potential to be realized directly using the physical prope
Abstract Gain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point a
