Kentaro Kutsukake 研究室

主宰者：Kentaro Kutsukake

名古屋大学

AI 要約（直近 5 年の研究成果）

この研究室は、シリコンおよび炭化ケイ素などの半導体結晶の成長・製造プロセスの最適化と、その品質向上に関する研究を行っています。特に、多層膜構造や微細な結晶欠陥の制御が課題となる薄膜太陽電池や高品質結晶の製造において、従来の試行錯誤的なアプローチではなく、物理的な洞察と数値シミュレーション・機械学習を組み合わせた効率的な設計・最適化手法を開発しています。主要な研究手法として、ベイズ最適化や遺伝的アルゴリズムなどの最適化技術、機械学習モデル（ニューラルネットワークや生成AI）による予測・解析、計算流体力学シミュレーション、および位相場モデルを用いた結晶成長過程の数値シミュレーションを活用しています。実験的には、レーザーを用いた熱物性測定や光学画像解析により、結晶の微視的特性を定量的に評価しています。主要な発見の方向性として、複数の物理パラメータを明示的に組み込んだ最適化スキームにより、プロセスパラメータが多い複雑な多層膜構造でも高精度な性能予測が可能であること、異なる製造装置間での最適条件の転移が可能であること、結晶粒界や転位クラスタなどのミクロ欠陥の生成メカニズムが結晶方位や粒界構造などの特定の結晶学的特徴と相関していることが報告されています。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（63 件）

[2026] Transfer of OptimizedCrystal-Growth Environmentsbetween Different Furnace Configurations via a Latent Space
DOI: https://doi.org/10.1021/acsomega.6c00093.s001
[2026] Regression framework incorporating intermediate parameters for improved prediction accuracy in multilayer thin films for silicon heterojunction solar cells with high efficiency
DOI: https://doi.org/10.35848/1347-4065/ae5f26
[2026] Bayesian Optimization–Driven Design of Hydrogen Concentration in A-Si/H Multilayers for Silicon Heterojunction Solar Cells
DOI: https://doi.org/10.1021/acsami.6c01687
[2026] Bayesian Optimization–DrivenDesign of HydrogenConcentration in A‑Si/H Multilayers for Silicon HeterojunctionSolar Cells
DOI: https://doi.org/10.1021/acsami.6c01687.s001
[2026] Transfer of Optimized Crystal-Growth Environments between Different Furnace Configurations via a Latent Space
DOI: https://doi.org/10.1021/acsomega.6c00093
[2025] A machine learning approach to reveal microstructures contributing to dislocation clusters in multicrystalline silicon
DOI: https://doi.org/10.1063/5.0260424
[2025] Determination of featured parameters for GaN surface morphologies by using a 2D growth simulation
DOI: https://doi.org/10.1016/j.vacuum.2025.114428
[2025] A design methodology of crystal growth furnace and process aided by two-step optimization using machine learning models and genetic algorithm
DOI: https://doi.org/10.1080/27660400.2025.2581358
[2025] Numerical Simulation of the Formation Process of Overhanging Structures in SiC Solution Growth to Suppress Solvent Inclusion
DOI: https://doi.org/10.1021/acs.cgd.5c00314
[2025] Dislocation cluster generation behavior in multicrystalline silicon investigated using twin network analysis
DOI: https://doi.org/10.1080/14686996.2025.2512703

続きを表示（残り 53 件）

[2025] Constrained Bayesian Optimization for the Deposition Conditions of Doped a-Si:H with High Passivation Performance for SHJ Solar Cells
DOI: https://doi.org/10.1021/acsami.5c05277
[2025] Dynamic deformation modeling for the interfaces of growth, etching, and deposition under switching flow in TSSG-SiC growth
DOI: https://doi.org/10.1039/d5ce00314h
[2025] Modeling and analysis of undoped GaN grown in a horizontal laminar flow MOCVD reactor
DOI: https://doi.org/10.1016/j.mssp.2024.109258
[2025] Machine learning-based laser heterodyne photothermal displacement method: simultaneous estimation of silicon thermal diffusivity and carrier lifetime
DOI: https://doi.org/10.35848/1347-4065/ada9f6
[2025] Two‐Stage GAN‐Based Generation of Virtual 3D Multicrystalline Silicon Reproducing Nucleation and Crystal Growth Processes Using Crystallographic Information from Real Ingots
DOI: https://doi.org/10.1002/aidi.202500012
[2024] Thermal boundary conductance of artificially and systematically designed grain boundaries of Silicon measured by laser heterodyne photothermal displacement method
DOI: https://doi.org/10.1063/5.0237047
[2024] Stress analysis and dislocation cluster generation in silicon crystal with artificial grain boundaries
DOI: https://doi.org/10.1016/j.jcrysgro.2024.127922
[2024] Feature extraction and spatial imaging of synchrotron radiation X-ray diffraction patterns using unsupervised machine learning
DOI: https://doi.org/10.1080/27660400.2024.2336402
[2024] Exploring mc‐Silicon Wafers: Utilizing Machine Learning to Enhance Wafer Quality Through Etching Studies
DOI: https://doi.org/10.1002/crat.202300279
[2024] High Passivation Performance of Cat-CVD i-a-Si:H Derived from Bayesian Optimization with Practical Constraints
DOI: https://doi.org/10.1021/acsami.3c16202
[2024] Analysis of Macrostep Interaction via Carbon Diffusion Field in SiC Solution Growth
DOI: https://doi.org/10.1016/j.jcrysgro.2024.127609
[2024] Effect of Solution Components on Solvent Inclusion in SiC Solution Growth
DOI: https://doi.org/10.1021/acs.cgd.3c01476
[2024] Review of machine learning applications for crystal growth research
DOI: https://doi.org/10.1016/j.jcrysgro.2024.127598
[2023] 3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon
DOI: https://doi.org/10.1063/5.0156044
[2023] Machine Learning for Semiconductor Process Simulation Described by Coupled Partial Differential Equations
DOI: https://doi.org/10.1002/adts.202300218
[2023] A machine learning-based prediction of crystal orientations for multicrystalline materials
DOI: https://doi.org/10.1063/5.0138099
[2023] Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells
DOI: https://doi.org/10.1186/s11671-023-03821-9
[2023] Modeling-Based Design of the Control Pattern for Uniform Macrostep Morphology in Solution Growth of SiC
DOI: https://doi.org/10.1021/acs.cgd.2c01194
[2023] Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation
DOI: https://doi.org/10.1002/adma.202308599
[2022] Bayesian Optimization for Cascade-Type Multistage Processes
DOI: https://doi.org/10.1162/neco_a_01550
[2022] Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC
DOI: https://doi.org/10.1002/adts.202200302
[2022] Study on electrical activity of grain boundaries in silicon through systematic control of structural parameters and characterization using a pretrained machine learning model
DOI: https://doi.org/10.1063/5.0086193
[2022] Estimation of Crystal Orientation of Grains on Polycrystalline Silicon Substrate by Recurrent Neural Network
DOI: https://doi.org/10.1002/tee.23676
[2022] A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth
DOI: https://doi.org/10.1002/adts.202200204
[2022] Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification
DOI: https://doi.org/10.1021/acsomega.1c06018
[2022] Virtual experiments of Czochralski growth of silicon using machine learning: Influence of processing parameters on interstitial oxygen concentration
DOI: https://doi.org/10.1016/j.jcrysgro.2022.126580
[2022] Effects of grain boundary structure and shape of the solid–liquid interface on the growth direction of the grain boundaries in multicrystalline silicon
DOI: https://doi.org/10.1039/d1ce01573g
[2022] Analysis of grain growth behavior of multicrystalline Mg2Si
DOI: https://doi.org/10.35848/1347-4065/aca032
[2021] 6 inch SiC bulk crystal grown by solution method aided by AI technology
[2021] Optimization of furnace temperature distribution in SiC sublimation process using machine learning
[2021] 3D multicrystalline Si modeling by experiments and data scientific methods and finite element stress analysis
[2021] Realization of high-performance passivating contact via Bayesian optimization: Application to TiO x /crystalline silicon heterostructure
[2021] Construction of characteristics prediction model for diamond semiconductor field effect transistors
[2021] Crystal orientation estimation model based on light reflection profile for multicrystalline silicon
[2021] Reduction of defects in silicon quantum dot multilayers using Bayesian optimization
[2021] Parameter identification for thermal fluid simulation in crystal growth using multi-data
[2021] Investigation of electrical characteristics of triple points in multicrystalline silicon via 3D carrier simulation
[2021] Self-supervised representation learning with grayscale images
[2021] Identification of Dislocation Clusters based on Image Translation of Photoluminescence Image of Multicrystalline Silicon Wafer
[2021] Application of Bayesian optimization for high-performance TiO /SiO /c-Si passivating contact
DOI: https://doi.org/10.1016/j.solmat.2021.111251
[2021] Effect of grain boundary plane orientation on carrier recombination velocity at asymmetric grain boundaries in multicrystalline silicon
[2021] Application of Machine Learning for Crystal Growth of Bulk and Film Silicon
DOI: https://doi.org/10.23919/am-fpd52126.2021.9499166
[2021] Mechanism for segregation of arsenic dopants at grain boundaries in silicon
[2021] Application of Bayesian optimization for improved passivation performance in TiO x /SiO y /c-Si heterostructure by hydrogen plasma treatment
DOI: https://doi.org/10.35848/1882-0786/abd869
[2021] Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth
DOI: https://doi.org/10.1039/d0ce01824d
[2021] Geometrical design of a crystal growth system guided by a machine learning algorithm
DOI: https://doi.org/10.1039/d1ce00106j
[2021] Segregation mechanism of arsenic dopants at grain boundaries in silicon
DOI: https://doi.org/10.1080/27660400.2021.1969701
[2021] Application of Bayesian optimization for high-performance TiOx /SiOy /c-Si passivating contact
[2021] Multicrystalline Informatics for High-Performance Crystals
DOI: https://doi.org/10.7567/ssdm.2021.k-4-01
[2021] A Systematic Study on the Growth Direction of Grain Boundaries during Solidification of Multicrystalline Silicon:Effects of Grain Boundary Structure and Shape of the Solid-Liquid Interface
DOI: https://doi.org/10.7567/ssdm.2021.k-4-02
[2021] Direct prediction of electrical properties of grain boundaries from photoluminescence profiles using machine learning
DOI: https://doi.org/10.1063/5.0049847
[2021] Optimization of flow distribution in SiC solution growth using topology and machine learning
[2021] Heat source layout optimization to lower the maximum temperature for electronic substrate

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（63 件）

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