Faculty Profiles - USAMI Yuki

Papers - USAMI Yuki

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Effect of electronic state for in-materio physical reservoir computing performance with a porphyrin-polyoxometalate/single-walled carbon nanotube network Reviewed International journal

Usami Y., Murazoe S., Banerjee D., Kotooka T., Tanaka H.

Applied Physics Letters 126 ( 8 ) 2025.02

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network Reviewed

Usami Y., van de Ven B., Mathew D.G., Chen T., Kotooka T., Kawashima Y., Tanaka Y., Otsuka Y., Ohoyama H., Tamukoh H., Tanaka H., van der Wiel W.G., Matsumoto T.

Advanced Materials 33 ( 48 ) 2102688 2021.09

In-materio Physical Reservoir Computing Utilizing Conductive Polymer Network Invited Reviewed

USAMI Yuki, KOTOOKA Takumi, MATSUMOTO Takuya, TANAKA Hirofumi

Vacuum and Surface Science ( The Japan Society of Vacuum and Surface Science ) 67 ( 11 ) 527 - 532 2024.11

Authorship：Lead author Language：Japanese Publishing type：Research paper (scientific journal)

Artificial intelligence (AI) has rapidly advanced and is being utilized across a wide range of fields by developing artificial neural network (ANN). However, constructing large-scale ANNs requires significant power consumption. In the field of materials and device engineering, “physical reservoir computing (physical RC)” is gaining attention to perform ANN computations with low power consumption. Unlike traditional ANNs, physical RC leverages physical properties for computation, resulting in energy efficiency. Conductive polymers such as polyaniline are expected to exhibit excellent performance in in-materio physical reservoir computing (IMRC) due to their superior environmental stability and reversible doping behavior. This paper introduces about utilizing the mixed polaron-ion conductivity of sulfonated polyaniline (SPAN), evaluating changes in electrical properties with humidity adjustments, and exploring its potential application in IMRC by investigating the correlation between the electrical properties and computational ability.

DOI： 10.1380/vss.67.527

CiNii Research

Other Link： http://id.ndl.go.jp/bib/033827434

Physical Reservoir Computing Devices Utilizing Nanomaterial Random Network and Its Sensing Application Invited

TANAKA Hirofumi, AZHARI Saman, BANERJEE Deep, KIMIZUKA Kouki, TANAKA Yuichiro, USAMI Yuki

Vacuum and Surface Science ( The Japan Society of Vacuum and Surface Science ) 67 ( 9 ) 428 - 432 2024.09

Language：Japanese Publishing type：Article, review, commentary, editorial, etc. (scientific journal)

The rapid advancement of software-based deep learning has led to a surge in AI applications, yet hardware limitations in silicon CMOS technology hinder performance. Consequently, interest is growing in hardware technologies and new materials for artificial neural networks (ANNs) and neuromorphic systems. Exploiting nanomaterials nonlinearity caused by spontaneous physical phenomena holds promise of reducing power consumption in AI hardware. Reservoir computing devices, derived from recurrent neural networks, play a crucial role, with material reservoir devices showcasing “material intelligence.” Tailored nanomaterials for reservoir devices hold potential for revolutionizing AI, especially in robotics. As research progresses, focusing on device functionalization and applications, recent findings underline the significance of integrating nanomaterials into AI hardware for enhanced computational capabilities and energy efficiency.

DOI： 10.1380/vss.67.428

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/vss/67/9/67_20181334/_pdf

A new physical reservoir using the complex dynamics of electric fields in type-II superconductors containing pinning centers interacting with quantized magnetic flux lines Reviewed International journal

Arita K., Ueda T., Otabe E.S., Usami Y., Tanaka H., Matsuno T.

Physica C: Superconductivity and its Applications 622 2024.07

High Performance of an In-Material Reservoir Computing Device Achieved by Complex Dynamics in a Nanoparticle Random Network Memristor Reviewed International journal

Srikimkaew O., Banerjee D., Azhari S., Usami Y., Tanaka H.

ACS Applied Electronic Materials 6 ( 2 ) 688 - 695 2024.02

Effect of nonlinearity induced by atomic switch in Ag/Ag<inf>2</inf>S nanoparticles on performance of in-materio reservoir computing Reviewed International journal

Dang T.T., Srikimkaew O., Banerjee D., Azhari S., Usami Y., Tanaka H.

Applied Physics Letters 124 ( 9 ) 2024.02

Visualizing Ribbon-to-Ribbon Heterogeneity of Chemically Unzipped Wide Graphene Nanoribbons by Silver Nanowire-Based Tip-Enhanced Raman Scattering Microscopy Reviewed International journal

Inose T., Toyouchi S., Hara S., Sugioka S., Walke P., Oyabu R., Fortuni B., Peeters W., Usami Y., Hirai K., De Feyter S., Uji-i H., Fujita Y., Tanaka H.

Small 20 ( 3 ) 2024.01

Short-Term and Long-Term Memory Functionality of a Brain-Like Device Built from Nanoparticle Atomic Switch Networks Invited Reviewed International journal

Srikimkaew O., Azhari S., Banerjee D., Usami Y., Tanaka H.

Advanced Electronic Materials 10 ( 12 ) 2024.01

Volatile and Nonvolatile Dual-Mode Switching Operations in an Ag-Ag<inf>2</inf>S Core-Shell Nanoparticle Atomic Switch Network Reviewed International journal

Srikimkaew O., Ricci S., Porzani M., Dang T.T., Nakaoka Y., Usami Y., Ielmini D., Tanaka H.

Advanced Electronic Materials 10 ( 10 ) 2024.01

Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor Invited Reviewed International journal

Tokuno Shoshi, Kimizuka Kouki, Tanaka Yuichiro, Usami Yuki, Tanaka Hirofumi, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( The Institute of Electronics, Information and Communication Engineers ) 15 ( 4 ) 883 - 898 2024.01

Language：English Publishing type：Research paper (scientific journal)

This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate.

DOI： 10.1587/nolta.15.883

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/nolta/15/4/15_883/_pdf

Mel-frequency cepstral coefficients feature extracted voice recognition task using atomic switch Ag/Ag2S device-based time-delayed reservoir computing Invited Reviewed International journal

Karacali Ahmet, Nakao Yusuke, Srikimkaew Oradee, Abdi Gisya, Szacilowski Konrad, Usami Yuki, Tanaka Hirofumi

Nonlinear Theory and Its Applications, IEICE ( The Institute of Electronics, Information and Communication Engineers ) 15 ( 4 ) 871 - 882 2024.01

Language：English Publishing type：Research paper (scientific journal)

Neuromorphic devices have diverse potential applications, such as brain-inspired computers and promising high-performance arithmetic systems with power saving. Reservoir computing (RC), a type of recurrent neural network (RNN), achieves learning by adjusting the weights between the intermediate and output layers. Time-delay reservoir computing introduces a delay and creates virtual nodes within the middle layer. The Ag/Ag2S nanoparticles function as nonlinear electrical devices, following the atomic switch principles of the time-delay system. Voice recognition was performed with 87.81% accuracy when six different people pronounced the same number, and 80.18% when the same person pronounced ten different numbers.

DOI： 10.1587/nolta.15.871

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/nolta/15/4/15_871/_pdf

Performance improvement of in-materio reservoir computing by noise injection Invited Reviewed International journal

Hakoshima M., Usami Y., Kotooka T., Tanaka H.

Japanese Journal of Applied Physics 62 2023.06

Integration of Wireless Power Transfer Technology With Hierarchical Multiwalled Carbon Nanotubes-Polydimethylsiloxane Piezo-Responsive Pressure Sensor for Remote Force Measurement Reviewed International journal

Azhari S., Kimizuka K., Mehes G., Usami Y., Hayashi Y., Tanaka H., Miyake T.

IEEE Sensors Journal 23 ( 7 ) 7902 - 7909 2023.04

分子が学ぶ画像認識 Invited

宇佐美雄生

化学 ( 株式会社化学同人 ) 78 ( 4 ) 59 - 60 2023.03

Influence of junction resistance on spatiotemporal dynamics and reservoir computing performance arising from an SWNT/POM 3D network formed via a scaffold template technique Reviewed International journal

Azhari S., Banerjee D., Kotooka T., Usami Y., Tanaka H.

Nanoscale 15 ( 18 ) 8169 - 8180 2023.02

Random network device fabricated using Ag2Se nanowires for data augmentation with binarized convolutional neural network Reviewed International journal

Kotooka T., Tanaka Y., Tamukoh H., Usami Y., Tanaka H.

Applied Physics Express 16 ( 1 ) 2023.01

In-material reservoir implementation of reservoir-based convolution Reviewed International journal

Tanaka Y., Usami Y., Tanaka H., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2023-May 2023.01

In-materio computing in random networks of carbon nanotubes complexed with chemically dynamic molecules: a review Invited Reviewed International journal

Hirofumi Tanaka, Saman Azhari, Yuki Usami, Deep Banerjee, Takumi Kotooka, Oradee Srikimkaew, Thien-Tan Dang, S Murazoe, R Oyabu, Kouki Kimizuka, Masaya Hakoshima

Neuromorphic Computing and Engineering ( IOP publishing ) 2 ( 2 ) 022002 2022.05

In-materio Reservoir Working at Low Frequencies in a Ag 2 S-island Network Invited Reviewed International journal

Motoharu Nakajima, Kazuki Minegishi, Yosuke Shimizu, Yuki Usami, Hirofumi Tanaka, Tsuyoshi Hasegawa

Nanoscale ( Royal Society of Chemistry ) 14 ( 20 ) 7634 - 7640 2022.05

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Papers - USAMI Yuki