出版者・発行元：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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Scopus

出版者・発行元：Journal of Crystal Growth  

The application of information science and technology has led to a paradigm shift in scientific and technological research and crystal growth is no exception. Various types of application research have been conducted, and research methods that combine real experiments and simulations with information techniques are becoming increasingly complex. In this paper, I focus on the application of information science and technology to the field of crystal growth. In the first half, I discuss the characteristics of process informatics, including applications to crystal growth, from the perspective of how it differs from materials informatics. In the second half, by reviewing various application studies to crystal growth, I aim to highlight the characteristics and discuss future issues.

DOI： 10.1016/j.jcrysgro.2024.127598

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掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society (ACS)  

DOI： 10.1021/acsami.3c16202

掲載種別：研究論文（学術雑誌）   出版者・発行元：Crystal Growth and Design  

In the solution growth method of silicon carbide, cellular structures and solvent inclusions are fatal defects. This study investigates the mechanism of how cosolvent chromium and additive aluminum influence the formation of cellular structures and inclusions via numerical simulations based on a phase field model. The simulation results indicate that introducing chromium into the solution increases the growth rate of the SiC crystals. The uneven distribution of chromium components near the macrostep edge is prone to trigger constitutional supersaturation, ultimately leading to cellular structures and inclusions. Constitutional supersaturation is more pronounced in solutions with a higher viscosity. Additionally, a small amount of additive aluminum increases the interfacial energy, enhancing the step stability by raising the potential barrier for curved steps and moderating step slopes. Experimental results demonstrate that a solution containing 40% chromium can increase the growth rate by three times compared to a pure Si solution. The Si0.59-Cr0.4-Al0.01 solution emerges as a promising candidate, maintaining a high growth rate while preserving step stability and effectively suppressing the development of cellular structures and inclusions.

DOI： 10.1021/acs.cgd.3c01476

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Advanced Materials  

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

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PubMed

記述言語：日本語   出版者・発行元：一般社団法人 人工知能学会  

<p>レベルセット推定はこれまでに得られた測定結果から次の測定点を決める適応的実験計画の一種であり，可能な限り少数のデータを用いて望ましい水準を満たさない領域を推定する問題である．レベルセット推定ではそれぞれの測定点を入力とし対応する測定結果を出力とするブラックボックス関数を考え，これまでに得られたデータから推定したサロゲート関数を用いてまだ測定していない測定点が閾値を超えるかどうかを予測する．このとき，レベルセット推定の効率は（１）次の測定点を決定する獲得関数，（２）レベルセット推定を停止するタイミングの２つによって決まる．本研究の目的はサロゲート関数が閾値を超える確率に基づいたレベルセット推定の停止基準を提案することである．提案する停止基準は任意の獲得関数に対して，サロゲート関数が閾値を超える裾確率を保証することができる．本論文ではいくつかのテスト関数に対して提案する停止基準がレベルセット推定を効率的に停止できることを示す．</p>

DOI： 10.11517/pjsai.jsai2024.0_2m5os2401

CiNii Research

掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/5.0156044

掲載種別：研究論文（学術雑誌）   出版者・発行元：Advanced Theory and Simulations  

Technology computer-aided design (TCAD) simulation is an important tool for the development of semiconductor devices. Based on coupled partial differential equations (PDEs) for behaviors, TCAD can calculate objects such as impurities, point defects, and electronic carriers in semiconductors. However, over recent years semiconductor devices have become increasingly miniaturized and complicated, resulting in much longer calculation times for TCAD. Machine learning is one technology that may be used to overcome this simulation cost problem. In this study, a neural network architecture is proposed that considers the structure of the coupled PDEs. Features representing each concentration distribution of the calculation objects are extracted by convolution operations and their reaction is modeled by channel attention. The performance of the proposed architecture and of conventional neural network models is evaluated using a simulation dataset generated by 1D coupled PDEs that models the diffusion and reaction of vacancies and interstitial atoms. In addition, the advantage of the method is discussed through the analyses of error correlations of the two predictions and attention coefficients. The machine learning method developed in this study will be applicable to other physics described by coupled PDEs and is expected to speed up the computation of simulations in various fields.

DOI： 10.1002/adts.202300218

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記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/oubutsu.92.6_369

CiNii Research

掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/5.0138099

掲載種別：研究論文（学術雑誌）   出版者・発行元：Japanese Journal of Applied Physics  

Multicrystalline Mg2Si crystal with a diameter of 15 mm was grown via vertical Bridgman method. To clarify the growth mechanism of the multicrystalline structure, the grain growth behavior of the crystal was analyzed. This was carried out through segmenting grains by mean shift clustering using the light intensity profile obtained from multiple optical reflection images of the wafers and stacking the segmented images through the growth direction. Further crystal orientation measurement revealed that a grain with a higher surface energy competitively expanded to the lateral direction during crystal growth. We speculated that the growth behavior occurred because the supercooling was high enough to show difference in each grain’s growth rate. This idea was supported by crystal growth simulation to show a tendency for the crystallization rate to increase toward the latter half growth stage, which is consistent with the assumption for crystal growth with high supercooling.

DOI： 10.35848/1347-4065/aca032

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記述言語：英語   出版者・発行元：Discover Nano  

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 (σp/σd) was calculated as the ratio of photoconductivity (σp) and dark conductivity (σd) 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 (VOC) 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

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PubMed

掲載種別：研究論文（学術雑誌）   出版者・発行元：Crystal Growth and Design  

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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記述言語：英語   出版者・発行元：Neural Computation  

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 mul-tistage 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 dif-ficult to search for the optimal controllable parameters for each stage ex-haustively. 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 inter-vals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider suspen-sion 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

掲載種別：研究論文（学術雑誌）   出版者・発行元：IEEJ Transactions on Electrical and Electronic Engineering  

To analyze crystal defects in polycrystalline silicon substrates, it is necessary to measure the crystal orientation at high speed. In this paper, we propose a method for simultaneous and fast estimation of the crystal orientation of an entire substrate by measuring the reflected light from a single rotation of a light source. The effectiveness of the proposed method is verified by the orientation estimation experiments. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

DOI： 10.1002/tee.23676

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その他リンク： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/tee.23676

掲載種別：研究論文（学術雑誌）   出版者・発行元：Advanced Theory and Simulations  

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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掲載種別：研究論文（学術雑誌）   出版者・発行元：Advanced Theory and Simulations  

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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掲載種別：研究論文（学術雑誌）   出版者・発行元：Journal of Applied Physics  

We report on the effects of grain boundary (GB) structures on the carrier recombination velocity at GB (vGB) 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. vGB 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 vGB would be relatively large among the three macroscopic parameters. In addition, the results for the ς5 and ς13 GBs suggested that the minimum vGB 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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掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier {BV}  

DOI： 10.1016/j.jcrysgro.2022.126580

掲載種別：研究論文（学術雑誌）  

DOI： 10.1039/d1ce01573g

Web of Science

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：ACS Omega  

The casting mono-like silicon (Si) grown by directional solidification (DS) is promising for high-efficiency solar cells. However, high dislocation clusters around the top region are still the practical drawbacks, which limit its competitiveness to the monocrystalline Si. To optimize the DS-Si process, we applied the framework, which integrates the growing experiments, transient global simulations, artificial neuron network (ANN) training, and genetic algorithms (GAs). First, we grew the Si ingot by the original recipe and reproduced it with transient global modeling. Second, predictions of the Si ingot domain from different recipes were used to train the ANN, which acts as the instant predictor of ingot properties from specific recipes. Finally, the GA equipped with the predictor searched for the optimal recipe according to multi-objective combination, such as the lowest residual stress and dislocation density. We also implemented the optimal recipe in our mono-like DS-Si process for verification and comparison. According to the optimal recipe, we could reduce the dislocation density and smooth the growth rate during the Si ingot growing process. Comparisons of the growth interface and grain boundary evolutions showed the decrease of the interface concavity and the multi-crystallization in the top part of the ingot. The well-trained ANN combined with the GA could derive the optimal growth parameter combinations instantly and quantitatively for the multi-objective processes.

DOI： 10.1021/acsomega.1c06018

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PubMed

記述言語：日本語   出版者・発行元：日本結晶成長学会  

DOI： 10.19009/jjacg.49-1-06

CiNii Research

記述言語：日本語   出版者・発行元：日本太陽光発電学会  

DOI： 10.57295/jpvsproc.1.0_50

記述言語：日本語   出版者・発行元：日本太陽光発電学会  

DOI： 10.57295/jpvsproc.1.0_125

記述言語：日本語   出版者・発行元：日本太陽光発電学会  

DOI： 10.57295/jpvsproc.1.0_52

掲載種別：研究論文（学術雑誌）   出版者・発行元：Solar Energy Materials and Solar Cells  

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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掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Physics Letters  

We present a machine learning model to directly predict the carrier recombination velocity, vGB, at the grain boundary (GB) from the measured photoluminescence (PL) intensity profile by training it with numerical simulation results. As the training dataset, 1800 PL profiles were calculated with a combination of random values of four material properties—vGB, the GB inclination angle, and the carrier diffusion lengths in the grains on both sides of the GB. In addition, the measured noise was modeled artificially and applied to the simulated profiles. A neural network was constructed with the inputs of the PL profile and the outputs of the four properties. This served as the solver of the reverse problem of the computational simulation. The coefficient of determination and the root mean squared error of vlog, which is the common logarithm of vGB, for the test dataset were 0.97 and 0.245, respectively. This prediction error was sufficiently low for the practical estimation of vGB. Moreover, the calculation time was reduced by a factor of 198 000 compared to conventional numerical optimization of repeating the computational simulations. By utilizing this fast prediction method, continuous evaluation of vGB along a GB was demonstrated. The finding is expected to advance scientific investigation of the electrical properties of local defects.

DOI： 10.1063/5.0049847

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences  

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.

DOI： 10.1587/transfun.2020IMP0010

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CiNii Research

掲載種別：研究論文（学術雑誌）   出版者・発行元：CrystEngComm  

In the design of a crystal growth system, the ability to efficiently regulate intertwined geometrical parameters is crucial for its successful development and commercialization. However, the traditional experimental and computational methods consume tremendous amounts of time and resources. To address this problem, a machine learning approach was developed in this study to accelerate the geometry optimization process. It was found that the combination of machine learning with a genetic algorithm could generate various possible solutions through a global search at a relatively high speed, which lie outside the solution range of the experimental optimization methods that are currently used. By applying this technique, an optimal geometrical design was obtained for a 150 mm top-seed solution growth system, indicating that the proposed method represents an innovative and attractive strategy for the development of crystal growth systems with superior characteristics.

DOI： 10.1039/d1ce00106j

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掲載種別：研究論文（学術雑誌）   出版者・発行元：CrystEngComm  

To design a time-dependent control recipe which can ensure consistently suitable growth conditions in an unsteady growth system with dynamic environmental changes, an adaptive control method based on high-speed machine learning prediction models was proposed and applied to the solution growth of SiC crystals. This approach comprised three parts, namely, a quasi-unsteady computational fluid dynamics (CFD) model for thermal and flow field simulation, machine learning models for approximating the simulation results and giving instant prediction, and an optimization algorithm for searching the optimal growth conditions. First, the evolution of the flow, temperature and carbon concentration fields over 50 h unsteady growth following an original recipe with fixed control parameters was analyzed by CFD simulation. Then, adaptive control was applied to design a time-dependent growth process with a 100-timestep sequence. The hybrid of machine learning models and CFD simulation accelerated the entire design and optimization process by 300 times, compared with CFD simulations alone. The adaptive control facilitated superior performance compared with the fixed recipe, where the single SiC crystal thickness increased by ∼30% and the growth interface was more uniform. Further, crucible dissolution and polycrystal precipitation were suppressed by ∼50%, enabling longer growth time and more stable growth. It is the first time that the importance of adaptive control during long-term SiC solution growth is discussed, and the method proposed in this study demonstrated the potential for real-time optimization in the future.

DOI： 10.1039/d0ce01824d

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記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/jsapmeeting.2021.1.0_3049

記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/jsapmeeting.2021.1.0_3048

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Physics Express  

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

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1080/27660400.2021.1969701

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Web of Science

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Physics Express  

Non-coherent Σ3{111} grain boundaries (GBs) with a positive deviation in the tilt angle (θ 110 > 70.5°) exhibit a high recombination activity in high-performance multicrystalline silicon ingots. Most of the GB segments are composed of edge-type dislocations with the Burgers vector b of a/3111, unlike Lomer dislocations with b = a/2110 observed for negative deviations, arranged on coherent Σ3{111} GB segments. Stretched 110 reconstructed bonds along the tilt axis are introduced so as not to form dangling bonds, and large strains are generated around the dislocation cores. Oxygen and carbon atoms segregating due to the strains would induce the recombination activity.

DOI： 10.35848/1882-0786/abd0a0

Scopus

その他リンク： https://iopscience.iop.org/article/10.35848/1882-0786/abd0a0/pdf

記述言語：日本語   出版者・発行元：日本結晶成長学会  

DOI： 10.19009/jjacg.48-3-04

CiNii Research

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ECS Transactions  

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

記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

<p>逐次最適化のための機械学習法であるベイズ最適化は，探索と活用をバランスよく行う最適化手法として，広く応用されている．本稿では，はじめにベイズ最適化の概要を解説したあと，実際の実験へのベイズ最適化の応用として，シリコンエピタキシャル膜の成長条件の最適化への適用を紹介する．特に，エンジニアがもつ専門知識や経験の活用を中心に説明する．</p>

DOI： 10.11470/oubutsu.89.12_711

CiNii Research

掲載種別：研究論文（学術雑誌）   出版者・発行元：Materials Today Communications  

We applied a Bayesian optimization (BO) to optimize the epitaxial growth process of Si thin films. The BO enables us to effectively explore the optimal film growth conditions considering several experimental parameters and their interactions. In this way we reduced the total number of experiments required in the optimization. The epitaxial growth rate was maximized while five quality parameters were maintained within an acceptable range. Additionally, we considered two practical issues: eliminating equipment errors and the time cost of the quality parameter evaluation. To overcome these issues, we adaptively conducted BO with different constraints for different situations. As a result of these optimizations, the crystal growth rate was increased to be approximately twice as high as that under standard conditions, while satisfying the five quality parameter conditions.

DOI： 10.1016/j.mtcomm.2020.101538

Web of Science

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Physics Express  

Three-dimensional distribution of grain boundaries (GBs) and generation sources of dislocation clusters is examined in a cast-grown high-performance multicrystalline silicon ingot for commercial solar cells. A significant number of dislocations are generated nearby some triple junctions of random angle GBs, although it is believed that such non-coherent GBs would not induce large strain during the cast growth. This explosive generation of dislocations would take place when the triple junctions are interacted with multiple Σ3{111} GBs. A segment of the random angle GB connected with a pair of Σ3{111} GBs nearby the triple junction would act as a dislocation source.

DOI： 10.35848/1882-0786/abbb1c

Web of Science

Scopus

その他リンク： https://iopscience.iop.org/article/10.35848/1882-0786/abbb1c/pdf

掲載種別：研究論文（学術雑誌）   出版者・発行元：Journal of Applied Physics  

We present a quantification method of carrier recombination velocity of a general grain boundary (GB) by combining steady state photoluminescence (PL) imaging and carrier simulation under uniform illumination. To improve the accuracy and versatility, inclination angles of GBs, which can be obtained from front and rear optical images, were included in the carrier simulation model. As a consequence, simulated PL profiles successfully reproduced the tendency of experiments to exhibit shift of the minimal position from the GB on the front surface to the direction of inclination. Furthermore, by fitting simulated PL profiles with experiments, the carrier recombination velocity was evaluated for various GBs based on the improved model to consider the inclination angle.

DOI： 10.1063/5.0017823

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Scopus

記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/jsapmeeting.2020.2.0_157

記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/jsapmeeting.2020.1.0_3607

掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.5139972

Scopus

記述言語：日本語   出版者・発行元：一般社団法人 人工知能学会  

<p>所望の構造出力（ここでは各要素が相関を持つ多次元ベクトルを指す）を達成する入力パラメータを見つける逆問題のための能動学習法を提案する． 理論的な貢献として，ブラックボックスなベクトル値の目的関数に対して出力の要素間の相関を明示的にガウス過程モデルに取り込むことにより，少ない観測点数で所望の構造出力とモデルによる予測との間の誤差を最小化するための新たな獲得関数を提案した．また，提案法を人工の形状探索問題，多出力のベンチマーク問題および炭化ケイ素結晶成長モデリングの実データを用いた成長速度分布の探索問題に適用し有効性を検証した．数値実験の結果，提案法は相関を考慮しないモデリングに比べて高速に所望の出力を発見できることを確認した．</p>

DOI： 10.11517/pjsai.jsai2020.0_2j1gs201

CiNii Research

掲載種別：研究論文（学術雑誌）  

DOI： 10.35848/1882-0786/abc6ec

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/pvsc45281.2020.9300738

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Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Physics Express  

We propose to utilize artificial neural network (ANN) to optimize positions of a limited number of sensors for accurate monitoring, and demonstrate its effectiveness by a case study of four thermocouples in a directional solidification furnace. Our concept consists of choosing the positions with ANN that has the lowest loss from a multiplicity of ANNs, which were trained by the simulated temperature distributions along the outer crucible wall. Interestingly, the top ten ranks of accurate predictions contain positions around the crucible's bottom to suggest the importance of measuring temperatures carefully around higherature gradients that is the boundary between different materials.

DOI： 10.7567/1882-0786/ab52a9

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Scopus

その他リンク： http://iopscience.iop.org/article/10.7567/1882-0786/ab52a9/pdf

掲載種別：研究論文（学術雑誌）   出版者・発行元：Organic Electronics  

Rubrene is a promising and archetypal organic semiconductor owing to its high reported hole mobility. However, this high mobility only exists in its crystalline state, with orders of magnitude reduction in disordered films. Thus, as it pertains to thin film polycrystalline rubrene, it is important to understand structure and the presence of disordered regions at grain boundaries. Here, we use scanning transmission X-ray microscopy (STXM) to investigate polycrystalline rubrene thin films with either platelet or spherulite morphology. The STXM images allow us to distinguish and quantify the arrangement of the local structure in the crystal. The analysis suggests that the platelet film has more oriented molecules in the crystal than in the spherulite phase. Also, at spherulite grain boundaries, we reveal a high number of misaligned molecules compared to the smooth boundary in the platelet case, with grain boundary sinuosity of 0.045 ± 0.002 and 0.139 ± 0.002 μm for the platelet and spherulite cases, respectively, which help to explain the higher mobility in the former case.

DOI： 10.1016/j.orgel.2019.07.021

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Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Japanese Journal of Applied Physics  

The dependence of substrate work function (WF) on organic-organic heterojunction (OOH) interface was investigated using ultraviolet and X-ray photoelectron spectroscopy. We studied the interface of boron subphthalocyanine chloride (SubPc)/α-sexithiophene (6T) deposited on MoO3, SiO2, Cs2CO3. We observed that MoO3 and Cs2CO3 induce a p-doping and n-doping, respectively, due to the WF position, that can generate charge transfer at the OOH interface. However, the same effect was not observed after annealing the organic layers. Using scanning transmission X-ray microscope combining with near-edge X-ray absorption fine structure, we could observe that SubPc film became well-ordered after annealing the thin film. Thus, we suggested that the control of charge transfer arises from the reduction of the molecules misorientation on the film that induces a reduction in the density of gap states.

DOI： 10.7567/1347-4065/aaffbf

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Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Solar Energy Materials and Solar Cells  

We report on our attempt to perform the three-dimensional (3D) visualization of dislocation clusters in multicrystalline silicon (mc-Si) ingot by processing photoluminescence (PL) images and analysis of dislocation clusters in mc-Si. As-sliced wafers prepared using a high-performance (HP) mc-Si ingot were sequentially measured by PL imaging with intentional superposition of reflection. Then, various image processing techniques were applied to all the PL images to extract dark regions, which most likely correspond to dislocation clusters, as well as microstructures. By 3D reconstruction using a large quantity of 2D images, we could successfully visualize the generation, propagation and annihilation of dislocation clusters in HP mc-Si ingot. In addition, relationship between source region of dislocation clusters and crystal orientation were investigated by combining data scientific and experimental approaches. As a result, it was suggested that small angle grain boundaries with angular deviation of less than 10 degrees cause the generation of dislocation clusters.

DOI： 10.1016/j.solmat.2018.06.008

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記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：公益社団法人 日本表面真空学会  

<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

掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：CS MANTECH 2019 - 2019 International Conference on Compound Semiconductor Manufacturing Technology, Digest of Papers  

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

掲載種別：研究論文（学術雑誌）   出版者・発行元：Handbook of photovoltaic silicon  

DOI： 10.1007/978-3-662-56472-1_35

Scopus

記述言語：日本語   出版者・発行元：一般社団法人 人工知能学会  

<p>試料表面の測定位置を変えて物理量の空間分布を求めるマッピング測定は、材料評価の基本的な方法である。通常、マッピングは等間隔な座標に沿って実行される。しかしながら、その場合、測定の目的に対して非効率的な測定点も含まれる。そこで、より少ない計測点からより確からしい物理量分布を得ることを目指した。本研究では、太陽電池用シリコンのキャリアライフタイムマッピングに境界位置を効率的に推定する手法であるLSEを適用し、低品質領域の推定を行った。</p>

DOI： 10.11517/pjsai.jsai2019.0_2p4j201

CiNii Research

掲載種別：研究論文（学術雑誌）   出版者・発行元：Journal of Crystal Growth  

Analysis of nucleation sites in multicrystalline silicon (mc-Si) grown by directional solidification is required for further grain refinement to reduce dislocation density. In this study, Voronoi diagrams were utilized to analyze nucleation sites of mc-Si grown by single-layer Si beads (SLSB)-seeding method. The grain distribution at the bottom of the ingot was almost reproduced by the weighted Voronoi diagram with a relaxation method to optimize the positions of generating points of the diagram, which correspond to the nucleation sites, and the difference of nucleation timing of each crystal grain. Comparison of the generating points with the optical image indicated that the nucleation started at the remarkably deep portions of the nucleation layer. Further grain refinement by SLSB-seeding method could be achieved by suppressing the formation of the remarkably deep portions of the nucleation layer.

DOI： 10.1016/j.jcrysgro.2018.07.028

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Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2018.08.001

Scopus

掲載種別：研究論文（学術雑誌）   出版者・発行元：Japanese Journal of Applied Physics  

We investigated the distributions of interstitial oxygen (Oi) and substitutional carbon (Cs) in high-performance (HP) multicrystalline Si (mc-Si) and monocrystalline-like Si (mono-like Si) and compared them with those in conventional mc-Si, grown using the same furnace. The Oi concentration in mono-like Si grown using a Czochralski (Cz) silicon seed was the highest among the three crystals. On the other hand, the Oi and Cs concentrations in HP mc-Si grown using Siemens Si incubation seeds were the same as those in conventional mc-Si. Therefore, it is considered that Oi incorporated into the growing Si crystal originates not only from the quartz crucible wall but also from the seed. Additionally, Oi and Cs in HP mc-Si grown on the incubation seeds with adequately low Oi and Cs concentrations are distributed similarly to those in conventional mc-Si grown under the same conditions. We believe that it is important to consider the Oi and Cs concentrations in the feed stock materials both for the seed and whole ingots in the seed-casting method.

DOI： 10.7567/JJAP.57.08RB19

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記述言語：日本語   出版者・発行元：公益社団法人 応用物理学会  

DOI： 10.11470/jsapmeeting.2018.1.0_3821

CiNii Research

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/pssb.201700473

Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1111/jmi.12602

Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4975814

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.7567/JJAP.56.030301

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/pip.2795

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4964440

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.7567/JJAP.54.08KD10

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4926966

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4921742

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1103/PhysRevB.91.235315

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2014.02.052

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1010/j.jo-vsgro.2013.11.033

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2013.10.033

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1109/JPHOTOV.2013.2281730

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4820140

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2013.03.024

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4792061

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.7567/APEX.6.025505

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4771927

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.4764928

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2012.06.034

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2012.02.004

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2012.01.051

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.3652891

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.scriptamat.2011.06.028

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.3576108

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2011.01.069

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2010.09.059

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2010.01.011

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.3276219

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1143/JJAP.49.04DP01

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2009.09.055

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1103/PhysRevB.80.174108

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.actamat.2009.03.033

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.3079504

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1143/APEX.1.075001

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.2816207

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.2710348

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.2320/matertrans.48.143

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.2433025

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1143/JJAP.45.1734

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2005.04.049

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.jcrysgro.2004.11.141

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1143/JJAP.44.L778

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1063/1.1784036

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.apsusc.2003.08.100

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1143/JJAP.42.L232

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