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

DOI： 10.1063/5.0237047

Open Access

Web of Science

Scopus

Language：English  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.jcrysgro.2024.127609

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Scopus

Language：English   Publisher：Crystal Research and Technology  

This paper provides a method for improving the photovoltaic conversion efficiency and optical attributes of silicon solar cells manufactured from as-cut boron doped p-type multi-crystalline silicon wafers using acid-based chemical texturization via machine learning. A decreased reflectance, which can be attained by the right chemical etching conditions, is one of the key elements for raising solar cell efficiency. In this work, the mc-Silicon wafer surface reflectance is obtained under (<2%) after optimization of wet chemical etching. The HF + HNO3 + CH3COOH chemical etchant is used in the ratio 1:3:2 at different conditions of the etching duration of 1 min, 2 min, 3 min, and 4 min, respectively. The as-cut boron doped p-type mc-silicon wafers are analysed with ultraviolet–visible spectroscopy, optical microscopy, Fourier transforms infrared spectroscopy, thickness profilometer, and scanning electron microscopy before and after etching. The chemical etching solution produces good results in 3 min etched wafer, with a reflectivity value of <2%.The reflectivity and optical images are inputs to the convolutional neural network model and the linear regression model to obtain the etching rate for better reflectivity. The classification model provides 99.6% accuracy and the regression model results in the minimum mean squared error (MSE) of 0.062.

DOI： 10.1002/crat.202300279

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Authorship：Lead author,　Last author,　Corresponding author   Language：English  

DOI： 10.1016/j.jcrysgro.2024.127598

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Scopus

Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acsami.3c16202

Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.cgd.3c01476

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Scopus

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

Abstract

A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen‐Alexander‐Sumino model. Thus, this research method reveals the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials.

DOI： 10.1002/adma.202308599

Open Access

Web of Science

Scopus

PubMed

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>Level set estimation is one of the adaptive experimental design that determines the next measurement point by using the obtained measurement results so far, and its task is to estimate the regions that do not satisfy the desired level using as few data as possible. Level set estimation considers a black box function with each measurement point as an input and the corresponding measurement result as an output, and predicts whether unmeasurement point exceeds the threshold using a surrogate function estimated from the dataset. The efficiency of level set estimation depends on (1) the acquisition function that determines the next measurement point and (2) the timing at which level set estimation is stopped. This study proposes a stopping criterion for level set estimation based on the probability that the surrogate function exceeds the threshold value. The proposed stopping criterion can guarantee a tail probability that the surrogate function exceeds the threshold for any acquisition function. This paper shows that the proposed stopping criterion can efficiently stop level set estimation for several test functions.</p>

DOI： 10.11517/pjsai.jsai2024.0_2m5os2401

CiNii Research

Publisher：Transactions on Machine Learning Research  

Level set estimation (LSE) the problem of identifying the set of input points where a function takes a value above (or below) a given threshold is important in practical applications. When the function is expensive to evaluate and black-box, the straddle algorithm, a representative heuristic for LSE based on Gaussian process models, and its extensions with theoretical guarantees have been developed. However, many existing methods include a confidence parameter, β1/2 t, that must be specified by the user. Methods that choose β1/2 t heuristically do not provide theoretical guarantees. In contrast, theoretically guaranteed values of β1/2 t need to be increased depending on the number of iterations and candidate points; they are conservative and do not perform well in practice. In this study, we propose a novel method, the randomized straddle algorithm, in which βt in the straddle algorithm is replaced by a random sample from the chi-squared distribution with two degrees of freedom. The confidence parameter in the proposed method does not require adjustment, does not depend on the number of iterations and candidate points, and is not conservative. Furthermore, we show that the proposed method has theoretical guarantees that depend on the sample complexity and the number of iterations. Finally, we validate the applicability of the proposed method through numerical experiments using synthetic and real data.

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1063/5.0156044

Publishing type：Research paper (scientific journal)  

DOI： 10.1002/adts.202300218

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Scopus

Language：Japanese   Publisher：公益社団法人 応用物理学会  

DOI： 10.11470/oubutsu.92.6_369

CiNii Research

Publishing type：Research paper (scientific journal)  

DOI： 10.1063/5.0138099

Publishing type：Research paper (scientific journal)  

DOI： 10.35848/1347-4065/aca032

Open Access

Web of Science

Scopus

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Silicon quantum dot multilayer (Si-QDML) is a promising material for a light absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley–Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS (σ_p/σ_d) was calculated as the ratio of photoconductivity (σ_p) and dark conductivity (σ_d) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage (V_OC) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS.

DOI： 10.1186/s11671-023-03821-9

Open Access

Web of Science

Scopus

PubMed

Other Link： https://link.springer.com/article/10.1186/s11671-023-03821-9/fulltext.html

Publishing type：Research paper (scientific journal)  

In the solution growth of the SiC crystal, macrosteps with sufficient height on an off-axis substrate are required to reduce defects and achieve a high-quality grown layer. However, over-developed macrosteps can induce new defects and adversely affect the crystal quality. To better understand and control the behavior of macrosteps corresponding to the control parameters of the growth system, a simulation method that consists of a global two-dimensional computational fluid dynamic (CFD) model, a local three-dimensional CFD model near the growth front, and a kinetics model that describes the movement of macrosteps on the crystal surface is proposed. The simulation method is first applied to investigate the effect of the crystal rotation speed on macrostep morphology. Although the results indicate that a higher crystal rotation speed results in less step bunching, constantly rotating the crystal in one direction is demonstrated to be incapable of yielding a uniform macrostep distribution on the whole surface. Accordingly, a sophisticated control pattern is designed by periodically switching the flow direction underneath the crystal surface, where the proposed simulation method is critical to determine detailed control-parameter values. When the control pattern suggested by the simulation is used, a grown crystal with a uniform macrostep morphology and ideal step height on the whole surface is obtained in the practical experiment.

DOI： 10.1021/acs.cgd.2c01194

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MIT Press  

Abstract

Complex processes in science and engineering are often formulated as multistage decision-making problems. In this letter, we consider a cascade process, a type of multistage decision-making process. This is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider suspension setting, an extension in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, the motivation for this study.

DOI： 10.1162/neco_a_01550

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PubMed

Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/tee.23676

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Scopus

Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/tee.23676

Publishing type：Research paper (scientific journal)  

The macroscopic distribution of fluid flows, which affect the quality of final products for various kinds of materials, is often difficult to describe in mathematical formulae and hinders the implementation of empirical knowledge in scaling up. In the present study, the characteristics of the flow distribution in silicon carbide (SiC) solution growth are described by using the position of the saddle point and the solution growth conditions are optimized by computational fluid dynamics simulation, machine learning, and a genetic algorithm. As a result, the candidates of the optimal condition for the solution growth of 6-in. SiC crystals are successfully obtained from the empirical knowledge gained from 3-in. crystal growth, by adding the topological description to the objective function. The present design of the objective function using the topological description can possibly be applied to other crystal growth or materials processing problems and to overcome scale-up difficulties, which can facilitate the rapid development of functional materials such as SiC wafers for power device applications.

DOI： 10.1002/adts.202200302

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Scopus

Publishing type：Research paper (scientific journal)  

Real-time prediction and dynamic control systems that can adapt to an unsteady environment are necessary for material fabrication processes, especially crystal growth. Recent studies have demonstrated the effectiveness of machine learning in predicting an unsteady crystal growth process, but its wider application is hindered by the large amount of training data required for sufficient accuracy. To address this problem, this study investigates the capability of transfer learning to predict geometric evolution in an unsteady silicon carbide (SiC) solution growth system based on a small amount of data. The performance of transferred models is discussed regarding the effect of the transfer learning method, training data amount, and time step length. The transfer learning strategy yields the same accuracy as that of training from scratch but requires only 20% of the training data. The accuracy is stably inherited through successive time steps, which demonstrates the effectiveness of transfer learning in reducing the required amount of training data for predicting evolution in an unsteady crystal growth process. Moreover, the transferred models trained with relatively more data (no more than 100%) further improve the accuracy inherited from the source model through multiple time steps, which broadens the application scope of transfer learning.

DOI： 10.1002/adts.202200204

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Scopus

Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

We report on the effects of grain boundary (GB) structures on the carrier recombination velocity at GB ( v_GB) in multicrystalline Si (mc-Si). The fabricated artificial GBs and an originally developed machine learning model allowed an investigation of the effect of three macroscopic parameters, misorientation angle α for Σ values, asymmetric angle β, and deviation angle θ from the ingot growth direction. Totally, 13 GBs were formed by directional solidification using multi-seeds with controlled crystal orientations. v_GB was evaluated directly from photoluminescence intensity profiles across GBs using a pre-trained machine learning model, which allowed a quantitative and continuous evaluation along GBs. The evaluation results indicated that the impact of θ on v_GB would be relatively large among the three macroscopic parameters. In addition, the results for the Σ5 and Σ13 GBs suggested that the minimum v_GB would be related to the GB energy. These results were discussed in terms of the complexity of the local reconstruction of GB structures. The deviation would make a more complex reconstructed GB structure with local distortion, resulting in an increase in the electrical activity of GBs. The obtained knowledge will contribute to improving various polycrystalline materials through a comprehensive understanding of the relationship between GB structures and their properties.

DOI： 10.1063/5.0086193

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Scopus

Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

DOI： 10.1016/j.jcrysgro.2022.126580

Publishing type：Research paper (scientific journal)   Publisher：Royal society of chemistry ({RSC})  

DOI： 10.1039/d1ce01573g

Web of Science

Language：English   Publishing type：Research paper (scientific journal)   Publisher：American chemical society ({ACS})  

DOI： 10.1021/acsomega.1c06018

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Scopus

PubMed

Language：Japanese   Publisher：The Japanese Association for Crystal Growth  

<p>  In this paper, machine learning and optimization based on simulation data of crystal growth are discussed from the viewpoint of informatics application, introducing our application to solution growth of SiC crystal. First, general aspects of crystal growth process simulation and its informatics applications are described. Next, after an overview of the solution growth of SiC crystal, the process optimization of solution growth of SiC crystal using machine learning is described, including the prediction model of temperature and flow of the solution in the crucible, the optimization of geometry conditions, and the optimization of process conditions corresponding to time evolution. Next, application to other materials is described. Finally, this paper is summarized with future prospects.</p>

DOI： 10.19009/jjacg.49-1-06

CiNii Research

Language：Japanese   Publisher：The Japan Photovoltaic Society  

DOI： 10.57295/jpvsproc.1.0_50

Language：Japanese   Publisher：The Japan Photovoltaic Society  

DOI： 10.57295/jpvsproc.1.0_52

Language：Japanese   Publisher：The Japan Photovoltaic Society  

DOI： 10.57295/jpvsproc.1.0_125

Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

We report on the application of Bayesian optimization (BO), which could accelerate the time-intensive process optimization of many parameters, to fabrication of the high-performance titanium oxide/silicon oxide/crystalline silicon passivating contact. The process contains pre-deposition treatment to form SiOy interlayer, atomic layer deposition (ALD) of TiOx, and hydrogen plasma treatment (HPT) as post-process. We attempted to optimize seven parameters for ALD and HPT by dealing with samples treated by three kinds of chemical solutions in the same batch. This permits to perform BO for each structure at the same time and determine the superior pre-deposition treatment. Consequently, carrier selectivity S10 estimated by independent measurements of the saturation current density and contact resistance was significantly improved by BO of only 12 cycles and 10 initial random experiments. These results certify that BO could efficiently provide experimental conditions in multidimensional parameter space although we need to consider the impact of the metallization process on the passivation performance.

DOI： 10.1016/j.solmat.2021.111251

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Scopus

Publishing type：Research paper (scientific journal)   Publisher：{AIP} publishing  

DOI： 10.1063/5.0049847

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Scopus

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

<p>In this paper, we evaluate a prediction method of regions including dislocation clusters which are crystallographic defects in a photoluminescence (PL) image of multicrystalline silicon wafers. We applied a method of a transfer learning of the convolutional neural network to solve this task. For an input of a sub-region image of a whole PL image, the network outputs the dislocation cluster regions are included in the upper wafer image or not. A network learned using image in lower wafers of the bottom of dislocation clusters as positive examples. We experimented under three conditions as negative examples; image of some depth wafer, randomly selected images, and both images. We examined performances of accuracies and Youden's J statistics under 2 cases; predictions of occurrences of dislocation clusters at 10 upper wafer or 20 upper wafer. Results present that values of accuracies and values of Youden's J are not so high, but they are higher results than ones of bag of features (visual words) method. For our purpose to find occurrences dislocation clusters in upper wafers from the input wafer, we obtained results that randomly select condition as negative examples is appropriate for 10 upper wafers prediction, since its results are better than other negative examples conditions, consistently.</p>

DOI： 10.1587/transfun.2020IMP0010

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Scopus

CiNii Research

Publishing type：Research paper (scientific journal)  

DOI： 10.1039/d1ce00106j

Web of Science

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1039/d0ce01824d

Web of Science

Scopus

Language：Japanese   Publisher：The Japan Society of Applied Physics  

DOI： 10.11470/jsapmeeting.2021.1.0_3049

Language：Japanese   Publisher：The Japan Society of Applied Physics  

DOI： 10.11470/jsapmeeting.2021.1.0_3048

Publishing type：Research paper (scientific journal)   Publisher：{IOP} publishing  

We applied hydrogen plasma treatment (HPT) on a titanium oxide/silicon oxide/crystalline silicon heterostructure to improve the passivation performance for high-efficiency silicon heterojunction solar cells. To accelerate the time-intensive process optimization of many parameters, we applied Bayesian optimization (BO). Consequently, the optimization of six process parameters of HPT was achieved by BO of only 15 cycles and 10 initial random experiments. Furthermore, the effective carrier lifetime after HPT on the optimized experimental conditions became three times higher compared with that before HPT, which certifies that BO is useful for accelerating optimization of the practical process conditions in multidimensional parameter space.

DOI： 10.35848/1882-0786/abd869

Web of Science

Scopus

Publishing type：Research paper (scientific journal)   Publisher：Informa {UK} limited  

DOI： 10.1080/27660400.2021.1969701

Web of Science

Web of Science

Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

DOI： 10.35848/1882-0786/abd0a0

Open Access

Scopus

Other Link： https://iopscience.iop.org/article/10.35848/1882-0786/abd0a0/pdf

Language：Japanese   Publisher：The Japanese Association for Crystal Growth  

<p>  We have been developing a SiC crystal growth technique using the solution method. As a result, we have achieved the growth of ultra-high quality crystals with extremely low dislocation density. The key to this is the reduction of dislocation density by utilizing the macro-step dislocation conversion phenomenon and the suppression of surface morphology roughness by controlling the flow in the solution. In order to put these technologies to practical use, we have developed a new machine learning technique for optimizing crystal growth conditions for large-diameter crystals. In this method, a model is constructed in the computer that reproduces the actual experiment quickly and accurately, and then hundreds of thousands or millions of trials are performed using the model to derive the experimental conditions with high efficiency. This means that optimization by surrogate models, which is one of the methods of process informatics, has been realized in crystal growth. By using these techniques, we were able to achieve 6-inch crystal growth in a very short time.</p>

DOI： 10.19009/jjacg.48-3-04

CiNii Research

Publishing type：Research paper (international conference proceedings)  

We report on our recent attempt to pioneer “multicrystalline informatics” through collaboration of experiments, theory, computation, and machine learning to establish universal guidelines how we can obtain high-performance multicrystalline materials. We employ silicon as a model material, and develop various useful machine learning models. One example is a neural network to predict distribution of crystal orientations in a large-area sample from multiple optical images. Transfer learning of pre-trained image classifier could predict spatial distribution of probability of dislocations generation from photoluminescence images. Extracted regions with high probability of dislocations generation could be characterized by multiscale experiments as well as computation using artificial-neural-network interatomic potential to disclose the physics behind. The obtained knowledge could be useful for process development of high-performance multicrystalline materials.

DOI： 10.1149/10204.0011ecst

Scopus

Language：Japanese   Publisher：The Japan Society of Applied Physics  

<p>Bayesian optimization, which is a machine learning method for sequential optimization, is widely applied as an optimization method that balances exploration and exploitation. In this paper, we first explain the outline of Bayesian optimization, and then introduce its application to the optimization of deposition conditions of epitaxial Si film growth. This paper focus on utilizing the expertise and experience of engineers.</p>

DOI： 10.11470/oubutsu.89.12_711

CiNii Research

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.mtcomm.2020.101538

Open Access

Web of Science

Scopus

Publishing type：Research paper (scientific journal)   Publisher：IOP publishing  

Abstract

We developed a machine learning model to predict interstitial oxygen (Oi) concentration in a Czochralski-grown silicon crystal. A highly accurate prediction can be ensured by selecting the appropriate experimental parameters that represent the change in the furnace conditions. A neural network was trained using the dataset of 450 ingots, and its prediction error for the testing dataset was 4.2 × 10¹⁶ atoms cm⁻³. Finally, a real-time prediction system was developed wherein the crystal growth data are input into the model, and the Oi concentration at the current growth interface is calculated immediately.

DOI： 10.35848/1882-0786/abc6ec

Scopus

Other Link： https://iopscience.iop.org/article/10.35848/1882-0786/abc6ec/pdf

Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

DOI： 10.35848/1882-0786/abbb1c

Open Access

Web of Science

Scopus

Other Link： https://iopscience.iop.org/article/10.35848/1882-0786/abbb1c/pdf

Publishing type：Research paper (scientific journal)   Publisher：{AIP} publishing  

DOI： 10.1063/5.0017823

Web of Science

Scopus

Language：Japanese   Publisher：The Japan Society of Applied Physics  

DOI： 10.11470/jsapmeeting.2020.2.0_157

Language：Japanese   Publisher：The Japan Society of Applied Physics  

DOI： 10.11470/jsapmeeting.2020.1.0_3607

Publishing type：Research paper (scientific journal)  

DOI： 10.1063/1.5139972

Scopus

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>We propose an active learning method for the inverse problem of finding input parameters that achieve the desired structured-output. Here, the structured-output refers to a multidimensional vector in which each element has a correlation. Specifically, we propose three acquisition functions to minimize the squared error between the desired structured-output and the prediction by the model by explicitly incorporating the correlation between output elements for a black-box vector-valued objective function into a Gaussian process model. We apply the proposed method to the search problem of growth rate distribution using actual data of silicon carbide (SiC) crystal growth modeling, and verify its effectiveness.</p>

DOI： 10.11517/pjsai.jsai2020.0_2j1gs201

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

Dislocation clusters that would degrade the electric property can be generated from a grain boundary (GB) neighboring a triple junction of GBs. The atomic plane of the GB is bent via the movement of the triple junction, supposedly due to S3{111} micro-twins intersecting the GB, and a number of dislocations would be generated nearby the bending corner. Bundles of dislocation arrays expanding nearly parallel to the growth direction and honeycombed dislocation networks lying on a {111} plane nearly normal to the growth direction can coexist, suggesting that multiple slip systems would be operated when the dislocations are tangled.

DOI： 10.1109/pvsc45281.2020.9300738

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Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

DOI： 10.7567/1882-0786/ab52a9

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Other Link： http://iopscience.iop.org/article/10.7567/1882-0786/ab52a9/pdf

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.orgel.2019.07.021

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Publishing type：Research paper (scientific journal)  

DOI： 10.7567/1347-4065/aaffbf

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Language：English   Publishing type：Part of collection (book)   Publisher：Springer berlin heidelberg  

The noncontact crucible (NOC) method has the potential to be an advanced cast method. It is effective in obtaining Si single ingots with large diameter and volume using cast furnace, and solar cells manufactured with Si obtained this way have high yield and high conversion efficiency. Several novel characteristics of this method are explained based on the existence of a large low-temperature region in a Si melt, which is key to realize its enclosing potential as follows. The largest diameter ratio of 0.9 was obtained by expanding the low-temperature region in the Si melt. For p-type solar cells, the highest of 19.14% and the average conversion efficiencies of 19.0% were obtained for the NOC wafers, using the same solar cell structure and process to obtain the conversion efficiency of 19.1% for a p-type Czochralski (CZ) wafers. The present method realized solar cells with conversion efficiency and yield as high as those of CZ solar cells using cast furnace for the first time. The latest information about the growth of Si ingots using the NOC method is explained.

DOI： 10.1007/978-3-662-56472-1_14

Scopus

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

DOI： 10.1016/j.solmat.2018.06.008

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Scopus

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japan Society of Vacuum and Surface Science  

<p>The prediction model of the result of computed fluid dynamics simulation in SiC solution growth was constructed on neural network using machine learning. Utilizing the prediction model, we can optimize quickly crystal growth conditions. In addition, the real-time visualization system was also made using the prediction model.</p>

DOI： 10.1380/vss.62.136

CiNii Books

CiNii Research

Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>For the task of estimating a spacial distribution of a physical quantity, it is common to x the measurement positions to meshgrid points evenly allocated along the coordinates of the space. However, such xed measurement positions often contain redundancy in the sense that not all the measurements in the meshgrid points are required for the target task. Especially when a measurement of the physical quantity is costly, it is thus benecial to allocate the measurement points adaptively and reduce the number of measurements. In this study, we applied Level Set Estimation (LSE), which is a method to efficiently estimate the boundary position, to carrier lifetime mapping of silicon for solar cells, and estimated the low quality region. Our approach can reasonably estimate the boundary position by measuring less than 1% position compare to conventional approach.</p>

DOI： 10.11517/pjsai.jsai2019.0_2p4j201

CiNii Research

Publishing type：Research paper (international conference proceedings)  

© 2019 CS Mantech. All rights reserved. We are developing solution growth technique for high-quality SiC bulk crystal. In actual, we have achieved high-quality crystal with very-low-density of threading dislocations grown by controlling the surface morphology. In order to apply this technique to large-scale crystal growth, it is necessary to control supersaturation at growth surface, flow rate and flow direction of solvent in detail. However, there are many growth parameters which should be optimized. Simulation technique based on computational fluid dynamics (CFD) is often used. However, it is still difficult to optimize growth condition by utilizing simulation technique since the calculation speed of CFD simulation is not enough to optimize the growth conditions, exhaustively. In recent, informatics including machine learning is applied to various fields including materials science. In this study, we tried to apply machine learning to the analysis of the results of CFD. We could make the model to optimize the crystal growth parameters based on a neural network model. Using the model, the optimization time became 10000 times faster. This is just a trial of “Process Informatics”.

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jcrysgro.2018.07.028

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Scopus

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

DOI： 10.1016/j.jcrysgro.2018.08.001

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.7567/JJAP.57.08RB19

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Scopus

Publisher：Applied Physics Express  

Variation in the formation energy of stacking faults (SFs) with the contamination of Na atoms was examined in Si crystals with different Fermi levels. Na atoms agglomerated at SFs under an electronic interaction, reducing the SF formation energy. The energy decreased with the decrease of the Fermi level: It was reduced by more than 10mJ/m2 in p-type Si, whereas it was barely reduced in n-type Si. Owing to the energy reduction, Na atoms agglomerating at SFs in p-type Si are stable compared with those in n-type Si, and this hypothesis was supported by ab initio calculations.

DOI： 10.7567/APEX.11.061303

Open Access

Scopus

DOI： 10.7567/APEX.11.061303

Web of Science

Language：Japanese   Publisher：The Japan Society of Applied Physics  

DOI： 10.11470/jsapmeeting.2018.1.0_3821

CiNii Research

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

DOI： 10.1002/pssb.201700473

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.7567/APEX.11.061303

Scopus

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

DOI： 10.1111/jmi.12602

Scopus

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

DOI： 10.1063/1.4975814

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Scopus

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

DOI： 10.7567/JJAP.56.030301

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

DOI： 10.1002/pip.2795

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

DOI： 10.1063/1.4964440

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

DOI： 10.7567/JJAP.54.08KD10

Web of Science

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

DOI： 10.1063/1.4926966

Web of Science

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

DOI： 10.1063/1.4921742

Web of Science

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

DOI： 10.1103/PhysRevB.91.235315

Web of Science

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

DOI： 10.1016/j.jcrysgro.2014.02.052

Web of Science

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

DOI： 10.1010/j.jo-vsgro.2013.11.033

Web of Science

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

DOI： 10.1016/j.jcrysgro.2013.10.033

Web of Science

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

DOI： 10.1109/JPHOTOV.2013.2281730

Web of Science

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

DOI： 10.1063/1.4820140

Web of Science

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

DOI： 10.1016/j.jcrysgro.2013.03.024

Web of Science

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

DOI： 10.1063/1.4792061

Web of Science

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

DOI： 10.7567/APEX.6.025505

Web of Science

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

Web of Science

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

Web of Science

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

DOI： 10.1063/1.4771927

Web of Science

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

DOI： 10.1063/1.4764928

Web of Science

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

DOI： 10.1016/j.jcrysgro.2012.06.034

Web of Science

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

DOI： 10.1016/j.jcrysgro.2012.02.004

Web of Science

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

DOI： 10.1016/j.jcrysgro.2012.01.051

Web of Science

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

DOI： 10.4028/www.scientific.net/KEM.508.220

Web of Science

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

Web of Science

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

DOI： 10.1063/1.3652891

Web of Science

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

DOI： 10.1016/j.scriptamat.2011.06.028

Web of Science

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

DOI： 10.1063/1.3576108

Web of Science

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

DOI： 10.1016/j.jcrysgro.2011.01.069

Web of Science

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

DOI： 10.1016/j.jcrysgro.2010.09.059

Web of Science

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

DOI： 10.1016/j.jcrysgro.2010.01.011

Web of Science

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

DOI： 10.1063/1.3276219

Web of Science

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

Web of Science

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

Web of Science

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

DOI： 10.1143/JJAP.49.04DP01

Web of Science

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

DOI： 10.1016/j.jcrysgro.2009.09.055

Web of Science

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

DOI： 10.1103/PhysRevB.80.174108

Web of Science

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

DOI： 10.1016/j.actamat.2009.03.033

Web of Science

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

DOI： 10.1063/1.3079504

Web of Science

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

DOI： 10.1143/APEX.1.075001

Web of Science

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

DOI： 10.1063/1.2816207

Web of Science

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

DOI： 10.1063/1.2710348

Web of Science

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

DOI： 10.2320/matertrans.48.143

Web of Science

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

DOI： 10.1063/1.2433025

Web of Science

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

DOI： 10.1143/JJAP.45.1734

Web of Science

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

DOI： 10.1016/j.jcrysgro.2005.04.049

Web of Science

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

DOI： 10.1016/j.jcrysgro.2004.11.141

Web of Science

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

DOI： 10.1143/JJAP.44.L778

Web of Science

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

DOI： 10.1063/1.1784036

Web of Science

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

DOI： 10.1016/j.apsusc.2003.08.100

Web of Science

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

DOI： 10.1143/JJAP.42.L232

Web of Science

Event date： 2025.2

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：DAE-Convention Centre   Country：India  

Event date： 2025.2

Language：English   Presentation type：Oral presentation (invited, special)  

Country：India  

Event date： 2025.2

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Sona College of Technology, Salem   Country：India  

Event date： 2025.1 - 2025.2

Language：English   Presentation type：Oral presentation (keynote)  

Country：India  

Event date： 2025.1

Language：English   Presentation type：Oral presentation (invited, special)  

Country：India  

Event date： 2025.1

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Mini Auditorium, SSN Institutions   Country：India  

Event date： 2024.12

Language：English   Presentation type：Oral presentation (invited, special)  

Event date： 2024.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2024.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2024.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2024.12

Language：English   Presentation type：Oral presentation (general)  

Country：United States  

Event date： 2024.11

Language：English   Presentation type：Poster presentation  

Country：Japan  

Event date： 2024.11

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：ソニックシティ（さいたま）   Country：Japan  

Event date： 2024.10 - 2024.11

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：大阪大学豊中キャンパス基礎工学国際棟   Country：Japan  

Event date： 2024.10

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：トークネットホール仙台   Country：Japan  

Event date： 2024.9

Language：English   Presentation type：Oral presentation (general)  

Country：Romania  

Event date： 2024.9

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Romania  

Event date： 2024.7

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2023.12

Language：Japanese  

Venue：東北大学金属材料研究所   Country：Japan  

Event date： 2023.12

Language：Japanese  

Venue：東北大学金属材料研究所   Country：Japan  

Event date： 2023.12

Language：Japanese  

Venue：ウインク愛知   Country：Japan  

Event date： 2023.12

Language：Japanese  

Venue：ウインク愛知   Country：Japan  

Event date： 2023.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2023.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2023.10

Language：Japanese  

Venue：ベルサール八重洲   Country：Japan  

Event date： 2023.10

Language：Japanese  

Venue：ベルサール八重洲   Country：Japan  

Event date： 2023.9

Language：Japanese  

Venue：愛知工業大学   Country：Japan  

Event date： 2023.9

Language：Japanese  

Venue：愛知工業大学   Country：Japan  

Event date： 2023.8

Language：Japanese  

Venue：ウインク愛知   Country：Japan  

Event date： 2023.8

Language：Japanese  

Venue：ウインク愛知   Country：Japan  

Event date： 2023.7 - 2023.8

Language：English  

Venue：University Campus (Complesso universitario di) San Giovanni a Teduccio　Corso Nicolangelo Protopisani   Country：Italy  

Event date： 2023.7 - 2023.8

Language：English  

Venue：University Campus (Complesso universitario di) San Giovanni a Teduccio　Corso Nicolangelo Protopisani   Country：Italy  

Event date： 2023.6

Language：English  

Country：Japan  

Event date： 2023.6

Language：English  

Country：Japan  

Event date： 2023.3

Language：English  

Venue：ISS institute   Country：India  

Event date： 2023.3

Language：English  

Venue：ISS institute   Country：India  

Event date： 2023.3

Language：English  

Venue：ISS institute   Country：India  

Event date： 2023.3

Language：English  

Venue：ISS institute   Country：India  

Event date： 2023.2

Language：Japanese  

Venue：大阪産業創造館   Country：Japan  

Event date： 2023.2

Language：Japanese  

Venue：大阪産業創造館   Country：Japan  

Event date： 2023.1

Language：English  

Venue：Erwin-Schrödinger-Zentrum - Humboldt-Universität zu Berlin   Country：Germany  

Event date： 2023.1

Language：English  

Venue：Erwin-Schrödinger-Zentrum - Humboldt-Universität zu Berlin   Country：Germany  

Event date： 2022.12

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2022.12

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2022.11

Language：English  

Event date： 2022.11

Language：English  

Event date： 2022.11

Language：Japanese  

Venue：JR ホテルクレメント徳島   Country：Japan  

Event date： 2022.11

Language：Japanese  

Venue：JR ホテルクレメント徳島   Country：Japan  

Event date： 2022.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2022.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2022.10

Language：Japanese  

Venue：ダイワロイヤルホテル　THE KASHIHARA   Country：Japan  

Event date： 2022.10

Language：Japanese  

Venue：ダイワロイヤルホテル　THE KASHIHARA   Country：Japan  

Event date： 2022.10

Language：English  

Event date： 2022.10

Language：English  

Event date： 2022.9

Language：Japanese  

Venue：名古屋大学   Country：Japan  

Event date： 2022.9

Language：Japanese  

Venue：名古屋大学   Country：Japan  

Event date： 2022.9

Language：Japanese  

Venue：東北大学, Web   Country：Japan  

Event date： 2022.9

Language：Japanese  

Venue：東北大学, Web   Country：Japan  

Event date： 2022.9

Language：Japanese  

Venue：東北大学, Web   Country：Japan  

Event date： 2022.9

Language：Japanese  

Venue：東北大学, Web   Country：Japan  

Event date： 2022.7

Language：English  

Venue：Paris   Country：France  

Event date： 2022.7

Language：English  

Venue：Paris   Country：France  

Event date： 2022.7

Language：Japanese  

Venue：名古屋大学, Web   Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：名古屋大学, Web   Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：ウインク愛知, Web   Country：Japan  

Event date： 2022.7

Language：Japanese  

Venue：ウインク愛知, Web   Country：Japan  

Event date： 2022.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2022.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2022.3

Language：Japanese  

Venue：青山学院大学, Web   Country：Japan  

Event date： 2022.3

Language：Japanese  

Venue：青山学院大学, Web   Country：Japan  

Event date： 2022.3

Language：Japanese  

Venue：青山学院大学, Web   Country：Japan  

Event date： 2022.3

Language：Japanese  

Venue：青山学院大学, Web   Country：Japan  

Event date： 2021.11 - 2021.12

Language：English  

Event date： 2021.11 - 2021.12

Language：English  

Event date： 2021.11

Language：English  

Event date： 2021.11

Language：English  

Event date： 2021.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.8

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.8

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.7

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.7

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.7

Language：English  

Event date： 2021.7

Language：English  

Event date： 2021.6 - 2021.7

Language：English  

Event date： 2021.6 - 2021.7

Language：English  

Event date： 2021.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.5

Language：English  

Event date： 2021.5

Language：English  

Event date： 2021.1

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2021.1

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.12

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.12

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.12

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.12

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.11

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.10

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.9 - 2020.10

Language：English  

Event date： 2020.9 - 2020.10

Language：English  

Event date： 2020.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.9

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.6

Language：Japanese  

Venue：Web   Country：Japan  

Event date： 2020.1

Language：Japanese  

Venue：産業技術総合研究所 臨海副都心センター   Country：Japan  

Event date： 2020.1

Language：Japanese  

Venue：産業技術総合研究所 臨海副都心センター   Country：Japan  

Event date： 2019.10 - 2019.11

Language：Japanese  

Venue：大阪大学   Country：Japan  

Event date： 2019.10 - 2019.11

Language：Japanese  

Venue：大阪大学   Country：Japan  

Event date： 2019.10 - 2019.11

Language：Japanese  

Venue：大阪大学   Country：Japan  

Event date： 2019.10 - 2019.11

Language：Japanese  

Venue：大阪大学   Country：Japan  

Event date： 2019.9

Language：Japanese  

Venue：北海道大学   Country：Japan  

Event date： 2019.9

Language：Japanese  

Venue：北海道大学   Country：Japan  

Event date： 2019.9

Language：Japanese  

Venue：北海道大学   Country：Japan  

Event date： 2019.9

Language：Japanese  

Venue：北海道大学   Country：Japan  

Event date： 2019.9

Language：English  

Country：Japan  

Event date： 2019.9

Language：English  

Country：Japan  

Event date： 2019.8

Language：Japanese  

Venue：箕面観光ホテル   Country：Japan  

Event date： 2019.8

Language：Japanese  

Venue：箕面観光ホテル   Country：Japan  

Event date： 2019.8

Language：Japanese  

Venue：エッサム神田ホール   Country：Japan  

Event date： 2019.8

Language：Japanese  

Venue：エッサム神田ホール   Country：Japan  

Event date： 2019.8

Language：English  

Venue：Winter park, Denver, USA   Country：United States  

Event date： 2019.8

Language：English  

Venue：Winter park, Denver, USA   Country：United States  

Event date： 2019.6

Language：Japanese  

Venue：産総研臨海副都心センター別館   Country：Japan  

Event date： 2019.6

Language：Japanese  

Venue：産総研臨海副都心センター別館   Country：Japan  

Event date： 2019.3

Language：Japanese  

Venue：東京工業大学大岡山キャンパス   Country：Japan  

Event date： 2019.3

Language：Japanese  

Venue：東京工業大学大岡山キャンパス   Country：Japan  

Event date： 2019.3

Language：Japanese  

Venue：東京工業大学大岡山キャンパス   Country：Japan  

Event date： 2019.3

Language：Japanese  

Venue：東京工業大学大岡山キャンパス   Country：Japan  

Event date： 2019.2

Language：English  

Venue：SSN Research Centre, SSN College of Engineering, Chennai, India   Country：India  

Event date： 2019.2

Language：English  

Venue：SSN Research Centre, SSN College of Engineering, Chennai, India   Country：India  

Event date： 2019.2

Language：Japanese  

Venue：名古屋大学ベンチャービジネスラボラトリ   Country：Japan  

Event date： 2019.2

Language：Japanese  

Venue：名古屋大学ベンチャービジネスラボラトリ   Country：Japan  

Event date： 2018.11

Language：English  

Country：Japan  

Event date： 2018.11

Language：English  

Country：Japan  

Event date： 2018.10 - 2018.11

Language：Japanese  

Venue：仙台戦災復興記念館   Country：Japan  

Event date： 2018.10 - 2018.11

Language：Japanese  

Venue：仙台戦災復興記念館   Country：Japan  

Event date： 2018.10 - 2018.11

Language：Japanese  

Venue：仙台戦災復興記念館   Country：Japan  

Event date： 2018.10 - 2018.11

Language：Japanese  

Venue：仙台戦災復興記念館   Country：Japan  

Event date： 2018.10

Language：English  

Venue：Daejeon Convention Center (DCC), Daejeon, Korea   Country：Korea, Republic of  

Event date： 2018.10

Language：English  

Venue：Daejeon Convention Center (DCC), Daejeon, Korea   Country：Korea, Republic of  

Event date： 2018.9

Language：Japanese  

Venue：名古屋工業大学   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋工業大学   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.9

Language：Japanese  

Venue：名古屋国際会議場   Country：Japan  

Event date： 2018.8

Language：Japanese  

Venue：東北大学金属材料研究所   Country：Japan  

Event date： 2018.8

Language：Japanese  

Venue：東北大学金属材料研究所   Country：Japan  

Event date： 2018.1

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：名古屋大学東山キャンパスＥＳ総合館ＥＳホール   Country：Japan  

Event date： 2017.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：明治大学 駿河台キャンパス　グローバルフロント   Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：福岡国際会議場   Country：Japan  

Language：Japanese  

Country：Japan  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：名古屋   Country：Japan  

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Kolkata   Country：India  

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Sendai   Country：Japan  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Presentation type：Poster presentation  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：産業技術総合研究所 中央事業所 東地区   Country：Japan  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：秋葉原UDXカンファレンス   Country：Japan  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：横浜市開港記念会館   Country：Japan  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：オンライン開催   Country：Japan  

Language：English   Presentation type：Oral presentation (invited, special)