Tsuyoshi HASEGAWA

Tsuyoshi HASEGAWA

Waseda University

H-index: 53

Asia-Japan

About Tsuyoshi HASEGAWA

Tsuyoshi HASEGAWA, With an exceptional h-index of 53 and a recent h-index of 28 (since 2020), a distinguished researcher at Waseda University, specializes in the field of Nanoscience, Nanodevices, Neuromorphic systems.

His recent articles reflect a diverse array of research interests and contributions to the field:

Influence of unique behaviors in an atomic switch operation on hardware-based deep learning

Simulation of a physical reservoir made of a Ag2S islands network

Classification of direct optical signal inputs by Ag2S island network reservoir

Development of a physical reservoir that operates by the diffusion of Cu cations

Implementation of rock-paper-scissors judgment systems with a Ag2S reservoir

Research on tactile sensation by physical reservoir computing with a robot arm and a Ag2S reservoir

Performance improvement of a Ag-ion controlled molecular-gap atomic switch by reducing a switching area for applying to a deep learning system

Yield improvement in fabrication of a molecular-gap atomic switch by eliminating potential leakage current paths

Tsuyoshi HASEGAWA Information

University

Waseda University

Position

Professor Dept. Applied Physics

Citations(all)

13063

Citations(since 2020)

4339

Cited By

10752

hIndex(all)

53

hIndex(since 2020)

28

i10Index(all)

125

i10Index(since 2020)

69

Email

University Profile Page

Waseda University

Tsuyoshi HASEGAWA Skills & Research Interests

Nanoscience

Nanodevices

Neuromorphic systems

Top articles of Tsuyoshi HASEGAWA

Influence of unique behaviors in an atomic switch operation on hardware-based deep learning

Authors

Keita Tomatsuri,Tsuyoshi HASEGAWA

Journal

Japanese Journal of Applied Physics

Published Date

2024/2/7

Hardware-based deep learning using neuromorphic elements are gathering much attention to substitute the standard von Neuman computational architectures. Atomic switches can be candidate for the operating elements due to their analog resistance change in nonlinear and non-volatile manner. However, there are also several concerns in using atomic switches, such as inaccuracies in resistance control and autonomous weight decay. These characteristics can cause unintentional changes of weights during the learning process. In this study, we simulated how these characteristics of atomic switches influence the accuracy and the power consumption of the deep leaning. By implementing the weight decay, the accuracy remained high despite of the high error level. Power consumption also improved with weight decay in high error level.

Simulation of a physical reservoir made of a Ag2S islands network

Authors

YUSUKE MURASE,Tsuyoshi HASEGAWA

Journal

Japanese Journal of Applied Physics

Published Date

2024/2/5

Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.

Classification of direct optical signal inputs by Ag2S island network reservoir

Authors

Risa Matsuo,Tsuyoshi HASEGAWA

Journal

Japanese Journal of Applied Physics

Published Date

2024/1/23

We have reported that a physical reservoir with a silver sulfide island network can classify simple patterns of an irradiated light without converting it to a voltage signal input. In this study, we conducted experiments to verify whether detection of dynamical change in an irradiating light, e.g., moving in a reservoir layer, can be available. We also investigated the potential of the reservoir to detect a position of light exposure and the wavelength dependence, in addition to the existence of exposure time of light to a reservoir. The technique was applied to a task whether characters could be recognized even if the irradiated position was changed.

Development of a physical reservoir that operates by the diffusion of Cu cations

Authors

Masaru Hayakawa,Tsuyoshi Hasegawa

Journal

Japanese Journal of Applied Physics

Published Date

2024/4/2

We developed a physical reservoir using Cu 2 S and Cu-doped Ta 2 O 5 as a material of a reservoir layer, in both of which Cu cations contribute to the reservoir operation. The reservoirs showed nonlinearity and short-term memory required as reservoirs. The memory capacity becomes maximum with the input frequency at around 10 4 Hz. The t-distributed stochastic neighbor embedding analysis revealed that a Cu 2 S reservoir can classify input of five bit pulse trains, and a Cu-doped Ta 2 O 5 reservoir can classify input of six bit pulse trains. These are longer than four bit pulse trains that a Ag 2 S island network reservoir achieved in our previous study. Using the superior performance, NARMA task was also carried out.

Implementation of rock-paper-scissors judgment systems with a Ag2S reservoir

Authors

Atsuhiro Mizuno,Yuki Ohno,Masaru Hayakawa,Kaiki Yoshimura,Tsuyoshi Hasegawa

Journal

Japanese Journal of Applied Physics

Published Date

2024/1/23

There is a growing demand for physical reservoirs that operate with low power consumption and low computational cost. We have conducted research on the basic properties of Ag 2 S reservoirs, which are a type of physical reservoir. However, little research has been conducted on their applications. In this study, as a first step toward the practical application of Ag 2 S reservoirs, we implemented two types of rock-paper-scissors judgment systems using Ag 2 S reservoirs. In these experiments, we were able to demonstrate fast learning in the reservoir by comparing the results with methods using a single-layer perceptron and a convolutional neural network. In addition, we could obtain a maximum accuracy rate of about 98%.

Research on tactile sensation by physical reservoir computing with a robot arm and a Ag2S reservoir

Authors

Kaiki Yoshimura,Tsuyoshi Hasegawa

Journal

Japanese Journal of Applied Physics

Published Date

2024/2/14

In recent years, physical reservoir computing has attracted much attention because of its low computational cost and low power consumption. In terms of social implementation of artificial intelligence, physical reservoir has a potential to meet the request, such as the need for AI robots to process information related to tactile sensation. It has been reported that a Ag 2 S polycrystalline thin film retains short-term memory and non-linearity when used as a physical reservoir. In this study, we applied the technique to tactile sensation by combining with a pressure sensor attached to a robot arm. In the object grasping task, a Ag 2 S physical reservoir enabled the objective recognition with the accuracy of 81.3%, although the task failed with linear regression of the direct output from the pressure sensor. We also demonstrate the potential of the system to detect anomalies in object grabbing.

Performance improvement of a Ag-ion controlled molecular-gap atomic switch by reducing a switching area for applying to a deep learning system

Authors

Naonari Tanimoto,Tsuyoshi Hasegawa

Journal

Japanese journal of applied physics

Published Date

2023/3/3

In today's advanced information society, hardware-based neuromorphic systems attract much attention for achieving more efficient information processing. Hardware-based neuromorphic systems need devices that change their resistance in an analog manner like biological synapses. A molecular-gap atomic switch exhibits analog resistance change over a wider range compared to other non-volatile memory devices. However, several issues remain with the device, such as in cyclic endurance and retention. In this study, we fabricated a molecular-gap atomic switch with a reduced switching area. We expected that the reduction would limit the number of Ag+ cations that contribute to a switching phenomenon and solve the remaining issues. The fabricated devices endured 1000 switching cycles and exhibited stable analog resistance change. Deep learning was successfully demonstrated using 293 fabricated …

Yield improvement in fabrication of a molecular-gap atomic switch by eliminating potential leakage current paths

Authors

Haruki Ishijima,Tsuyoshi Hasegawa

Journal

Japanese journal of applied physics

Published Date

2023/2/14

A molecular-gap atomic switch is one of the emerging devices that works as a synaptic device. It shows good enough performance such as analog resistance change over five orders of magnitude. However, low yield in device fabrication due to short-circuit of as-fabricated devices has been a big issue. In this study, we Investigated the causes of the low yield and found several possible leakage current paths in unexpected routes. A new device structure and fabrication processes that eliminate the potential leakage paths were proposed. Operating characteristics were evaluated at each step in the improvement, and finally yield in the device fabrication was improved from 10% to 80%.

Ag2S island network reservoir that works with direct optical signal inputs

Authors

Yosuke Shimizu,Kazuki Minegishi,Hirofumi Tanaka,Tsuyoshi Hasegawa

Journal

Japanese journal of applied physics

Published Date

2023/1/11

A physical reservoir that accepts direct light irradiation as input was developed using a Ag 2 S island network. Short-term memory and nonlinearity required for reservoirs are achieved by the diffusion of Ag+ cations in each Ag 2 S island and the growth of Ag filaments between Ag 2 S islands. We found that direct light irradiation to Ag 2 S islands changes local conductivity in a reservoir, which enhances the performance in short-term memory and nonlinearity of the reservoir. Using the effect, we performed a pattern classification of light that was irradiated to a Ag 2 S island network reservoir through a rectangular slit, which resulted in the accuracy of over 95%.

Pulse width dependent operations of a Ag2S island network reservoir

Authors

Kazuki Minegishi,Yosuke Shimizu,Tsuyoshi Hasegawa

Journal

Japanese journal of applied physics

Published Date

2023/3/20

The rapid growth in demand for edge artificial intelligence increases importance of physical reservoirs that work at low computational cost with low power consumption. A Ag 2 S island network also works as a physical reservoir, in which various physicochemical phenomena contribute to a reservoir operation. In this study, we investigated its frequency dependence and found that diffusion of Ag+ cations in a Ag 2 S island, which has a relaxation time of about 100 μs, plays a major role when performance is improved. Modified National Institute of Standards and Technology (MNIST) classification task using an input pulse width of 100 μs resulted in the accuracy of 91%. Iterative operations up to 10 million cycles revealed a small enough standard deviation of output, suggesting a potential for practical use of a Ag 2 S island network as a reservoir.

In-materio reservoir working at low frequencies in a Ag2S-island network

Authors

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

Journal

Nanoscale

Published Date

2022

A Ag2S-island network is fabricated with surrounding electrodes to enable it to be used as a reservoir for unconventional computing. Local conductance change occurs due to the growth/shrinkage of Ag filaments from/into each Ag2S island in the reservoir. The growth/shrinkage of Ag filaments is caused by the drift of Ag+ cations in each Ag2S island, which results in a unique non-linear response as a reservoir, especially at lower frequencies. The response of the reservoir is shown to depend on the frequency and amplitude of the input signals. So as to evaluate its capability as a reservoir, logical operations were performed using the subject Ag2S-island network, with the results showing an accuracy of greater than 99%.

Behavioral Model of Molecular Gap-Type Atomic Switches and Its SPICE Integration

Authors

Hiroshi Kubota,Tsuyoshi Hasegawa,Megumi Akai-Kasaya,Tetsuya Asai

Journal

Circuits and Systems

Published Date

2022/1/30

Atomic switches can be used in future nanodevices and to realize conceptually novel electronics in new types of computer architecture because of their simple structure, ease of operation, stability, and reliability. The atomic switch is a single solid-state switch with inherent learning abilities that exhibits various nonlinear behaviors with network devices. However, previous studies focused on experiments and nonvolatile memory applications, and studies on the application of the physical properties of the atomic switch in computing were nonexistent. Therefore, we present a simple behavioral model of a molecular gap-type atomic switch that can be included in a simulator. The model was described by three simple equations that reproduced the bistability using a double-well potential and was able to easily be transferred to a simulator using arbitrary numerical values and be integrated into HSPICE. Simulations using the experimental parameters of the proposed atomic switch agreed with the experimental results. This model will allow circuit designers to explore new architectures, contributing to the development of new computing methods.

Ionic Nanoarchitectonics for Artificial Intelligence Devices

Authors

Kazuya Terabe,Tohru Tsuruoka,Takashi Tsuchiya,Tsuyoshi Hasegawa

Published Date

2022

IonicIonics devices that operate by ion transport in solids have been studied extensively as energy devices such as all-solid-state batteries and fuel cells, but in recent years, they have also attracted attention as next-generation artificial intelligence devicesArtificial intelligence device. By controlling local ion transport near interfaces between electrodes and ionicIonics conductors, interesting properties, which are not available with conventional semiconductor devices, are obtained. We call this method as ionic nanoarchitectonicsIonic nanoarchitectonics. Among the nanoionic devicesNanoionics device with various properties created by the ionic nanoarchitectonicsIonic nanoarchitectonics, atomic switchesAtomic switchand artificial intelligence devicesArtificial intelligence device such as artificial synapsesArtificial synapseand decision-making devicesDecision-making device are introduced.

Noise sensitivity of physical reservoir computing in a ring array of atomic switches

Authors

Hiroshi Kubota,Tsuyoshi Hasegawa,Megumi Akai-Kasaya,Tetsuya Asai

Journal

Nonlinear Theory and Its Applications, IEICE

Published Date

2022

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.

Rainer Waser–A Pioneer of Fundamentals of Resistive Switching Memories

Authors

Ilia Valov,Tsuyoshi Hasegawa,Dmitri Strukov

Published Date

2022/8

DOI: 10.1002/aelm. 202200765 project on high-epsilon materials for DRAMs as the only non-US citizen participant. He has therefore also received an award from the IEEE in 2000 for working in these areas. In 1997, Rainer Waser took over the management of the currently named “Institute for Electronic Materials”(PGI-7) at Forschungszentrum Jülich. In 2001 was appointed to lead the organization of the IFF Spring School named “New Materials in Information Technology” being also the title of his well-known textbook “Nanoelectronics and Information Technology”. He always has directed his research group on promising resistive switching in the following years. Through a first publication in 2006 [1] and especially his publication together with Masakazu Aono in 2007,[2] he established an explanation for resistive switching, which has been known since the 1960s. He was able to show that an oxygen ion movement …

Operating Mechanism and Resistive Switching Characteristics of Two-and Three-Terminal Atomic Switches Using a Thin Metal Oxide Layer

Authors

Tohru Tsuruoka,Tsuyoshi Hasegawa,Kazuya Terabe,Masakazu Aono

Published Date

2022

Atomic switches are nanoionic devices that are operated by controlling redox reactions and the local migration of metal ions in solids. The essential mechanism is the growth and shrinkage of a metal filament formed between two electrodes, resulting in repeatable resistive switching between high-resistance and low-resistance states, which can be used for next-generation nonvolatile memories. This review focuses on the operating mechanism and resistive switching characteristics of two- and three-terminal atomic switches using a thin metal oxide layer as an ion-conducting matrix. First, we describe the operating mechanism of a two-terminal atomic switch based on nucleation theory and present the results of temperature dependence and switching speeds to determine the validity of our switching model. Then, we discuss the effects that moisture absorption in the oxide matrix has on the fundamental …

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications (Adv. Mater. 32/2022)

Authors

Gianluca Milano,Masakazu Aono,Luca Boarino,Umberto Celano,Tsuyoshi Hasegawa,Michael Kozicki,Sayani Majumdar,Mariela Menghini,Enrique Miranda,Carlo Ricciardi,Stefan Tappertzhofen,Kazuya Terabe,Ilia Valov

Published Date

2022/8

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next‐generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena …

Study on a conductive channel of a Pt/NiO/Pt ReRAM by bias application with/without a magnetic field

Authors

Yuki Koga,Tsuyoshi Hasegawa

Journal

Japanese Journal of Applied Physics

Published Date

2021/3/4

Resistive random access memories (ReRAMs) have attracted much attention as a next-generation non-volatile memory. We focused on a NiO-based ReRAM in this study because it contains the magnetic element Ni. As-fabricated devices exhibit ideal memristive operation. When bias was swept in one polarity, the resistance decreased by repeating the bias sweeping. Conversely, by changing the polarity of the sweeping bias, the resistance gradually increased by repeating the bias sweeping. A steep increase in current was observed when continuing bias sweeping in the polarity that decreased the resistance. The resistance after that was lower than 12.9 kΩ, which suggests the formation of a Ni atom chain. Conductance quantization, with a unit of 2e 2/h, also suggested the said formation. When a magnetic field was applied, the unit of conductance quantization appeared to change from 2e 2/h to e 2/h.

Resistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability

Authors

Zheng Wang,Wei Xiao,Huiyong Yang,Shengjie Zhang,Yukun Zhang,Kai Sun,Ting Wang,Yujun Fu,Qi Wang,Junyan Zhang,Tsuyoshi Hasegawa,Deyan He

Journal

ACS Applied Materials & Interfaces

Published Date

2021/12/27

Ion-based memristive switching has attracted widespread attention from industries owing to its outstanding advantages in storage and neuromorphic computing. Major issues for achieving brain-inspired computation of highly functional memory in redox-based ion devices are relatively large variability in their operating parameters and limited cycling endurance. In some devices, volatile and nonvolatile operations often replace each other without changing operating conditions. To address these issues, it is important to observe directly what is happening in repeated operations. Herein, we use a planar device that enables direct capturing of microscopic behaviors in the nucleation and growth of metal whiskers under repeated switching to verify the microscopic origin of the large parameter variability. We report direct observations that reveal the physical origin for the large cycle-to-cycle and device-to-device variability …

Emulating neural functions utilizing the larger time constants found in the operation of molecular-gap atomic switches

Authors

Naoya Wada,Tsuyoshi Hasegawa

Journal

Japanese Journal of Applied Physics

Published Date

2021/2/22

Using hardware to emulate biological functions is essential for the realization of more sophisticated brain-type information processing. For this purpose, up to now, various nonvolatile devices have been used to emulate complex functions such as spike-timing dependent plasticity. However, little research has been conducted on more complicated neural functions. In this study, we demonstrate neural functions such as paired-pulse facilitation (PPF) and paired-pulse depression (PPD), utilizing the larger time constant of the ionic diffusion found in molecular-gap atomic switches. Both the PPF and PPD emulated in this study are dependent on pulse intervals that are the same as those found in biological synapses. Simulations of how pulsed bias changes ion concentration at the subsurface, which in turn determines the precipitation/dissolution of metal atoms, provide a good explanation of the mechanisms of the PPF …

See List of Professors in Tsuyoshi HASEGAWA University(Waseda University)

Tsuyoshi HASEGAWA FAQs

What is Tsuyoshi HASEGAWA's h-index at Waseda University?

The h-index of Tsuyoshi HASEGAWA has been 28 since 2020 and 53 in total.

What are Tsuyoshi HASEGAWA's top articles?

The articles with the titles of

Influence of unique behaviors in an atomic switch operation on hardware-based deep learning

Simulation of a physical reservoir made of a Ag2S islands network

Classification of direct optical signal inputs by Ag2S island network reservoir

Development of a physical reservoir that operates by the diffusion of Cu cations

Implementation of rock-paper-scissors judgment systems with a Ag2S reservoir

Research on tactile sensation by physical reservoir computing with a robot arm and a Ag2S reservoir

Performance improvement of a Ag-ion controlled molecular-gap atomic switch by reducing a switching area for applying to a deep learning system

Yield improvement in fabrication of a molecular-gap atomic switch by eliminating potential leakage current paths

...

are the top articles of Tsuyoshi HASEGAWA at Waseda University.

What are Tsuyoshi HASEGAWA's research interests?

The research interests of Tsuyoshi HASEGAWA are: Nanoscience, Nanodevices, Neuromorphic systems

What is Tsuyoshi HASEGAWA's total number of citations?

Tsuyoshi HASEGAWA has 13,063 citations in total.

What are the co-authors of Tsuyoshi HASEGAWA?

The co-authors of Tsuyoshi HASEGAWA are James K. Gimzewski, Bilge Yildiz, Taichi Okuda, Rui Yang, Hirofumi Tanaka, Stefan Tappertzhofen.

    Co-Authors

    H-index: 92
    James K. Gimzewski

    James K. Gimzewski

    University of California, Los Angeles

    H-index: 57
    Bilge Yildiz

    Bilge Yildiz

    Massachusetts Institute of Technology

    H-index: 50
    Taichi Okuda

    Taichi Okuda

    Hiroshima University

    H-index: 34
    Rui Yang

    Rui Yang

    Huazhong University of Science and Technology

    H-index: 29
    Hirofumi Tanaka

    Hirofumi Tanaka

    Kyushu Institute of Technology

    H-index: 23
    Stefan Tappertzhofen

    Stefan Tappertzhofen

    Technische Universität Dortmund

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