多尺度晶体材料的原位表征技术与计算模拟研究进展

doi:10.15541/jim20220647

无机材料学报 ›› 2023, Vol. 38 ›› Issue (3): 256-269.DOI: 10.15541/jim20220647

多尺度晶体材料的原位表征技术与计算模拟研究进展

陈昆峰¹(), 胡乾宇¹, 刘锋², 薛冬峰²()

1.山东大学新一代半导体材料研究院晶体材料国家重点实验室, 济南 250100
2.中国科学院深圳先进技术研究院, 多尺度晶体材料研究中心, 深圳 518055

收稿日期:2022-11-01 修回日期:2022-12-20 出版日期:2023-01-19 网络出版日期:2023-01-19
通讯作者: 薛冬峰, 研究员. E-mail: df.xue@siat.ac.cn
作者简介:陈昆峰(1987-), 教授. E-mail: kunfeng.chen@sdu.edu.cn
基金资助:
国家自然科学基金(51832007);国家自然科学基金(52220105010);国家自然科学基金(52202012);山东省自然科学基金重大基础研究项目(ZR2020ZD35);山东大学齐鲁青年学者项目

Multi-scale Crystallization Materials: Advances in in-situ Characterization Techniques and Computational Simulations

CHEN Kunfeng¹(), HU Qianyu¹, LIU Feng², XUE Dongfeng²()

1. State Key Laboratory of Crystal Materials, Institute of Novel Semiconductors, Shandong University, Jinan 250100, China
2. Multiscale Crystal Materials Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received:2022-11-01 Revised:2022-12-20 Published:2023-01-19 Online:2023-01-19
Contact: XUE Dongfeng, professor. E-mail: df.xue@siat.ac.cn
About author:CHEN Kunfeng(1987-), professor. E-mail: kunfeng.chen@sdu.edu.cn
Supported by:
National Natural Science Foundation of China(51832007);National Natural Science Foundation of China(52220105010);National Natural Science Foundation of China(52202012);Natural Science Foundation of Shandong Province(ZR2020ZD35);Qilu Young Scholars Program of Shandong University

摘要/Abstract

摘要：

大尺寸晶体材料是半导体、激光、通讯等领域的基础原料, 大尺寸、高品质晶体材料的制备已成为制约相关行业发展的瓶颈。我国面临的“卡脖子”技术中大多与关键基础材料相关。大尺寸晶体材料制备理论与技术是我国新材料产业高质量发展的一个重要方面, 也是提升相应高技术产业的基础, 突破大尺寸晶体材料的制备理论和技术是获得高品质大尺寸晶体材料的关键。探究并准确理解大尺寸晶体生长机理需要借助原位表征技术和多尺度计算模拟方法。单一的原位表征和模拟技术只能探究特定时间和空间范围内的结晶信息, 为了准确反映结晶过程需要综合应用多种方法。本文综述了最新的多尺度晶体生长研究的原位表征方法、多尺度计算模拟技术以及机器学习方法, 为发展结晶理论和控制晶体品质提供重要的实验和理论依据, 并将为提升大尺寸晶体生长工艺的开发而服务。

关键词: 晶体生长, 多尺度结晶, 振动光谱, 原位观测, 多尺度模拟计算, 综述

Abstract:

Large-sized crystalline materials are the basic raw materials in semiconductors, lasers, and communications. Preparation of large-scale, high-quality crystalline materials has become a bottleneck restricting the development of related industries. Breaking through the preparation theory and technology of large-sized crystal materials is the key to obtaining high-quality large-sized crystals. Preparation process of crystal materials often undergoes nucleation and growth stages, including multiple processes at spatiotemporal scale: from atom/molecules, through clusters and nuclei, to bulk crystals. To further explore and accurately understand the crystal growth mechanism, we need intensively study the multiscale process,multi-scale in situ characterization techniques, and computational simulation methods. Among them, the latest in situ characterization methods for crystal growth includes optical microscopy, electron microscopy, vibration spectra, synchrotron radiation, neutron technology, and especially, machine learning method. Thus, the multi-scale computational simulation techniques for crystallization is introduced, for example, first principles calculation at atom/molecular scale, molecular dynamics simulation, Monte Carlo simulation, phase field simulation at mesoscopic scale, and finite element simulation at macroscopic scale. A single in situ characterization or simulation technique can only explore crystallization information over a specific time and space scale. To accurately and fully reflect the crystallization process, a combination of multi-scale methods is introduced. It can be speculated that the establishment of in situ characterization technology and computational simulation methods for the actual large-sized crystal growth environment will be the future development trend, which provides an important experimental and theoretical basis for developing crystallization theory and controlling crystal quality. Furthermore, it can be deduced that the combination of in situ characterization technology with machine learning and big data technology will be the trend for large-sized crystal growth.

Key words: crystal growth, multi-scale crystallization, vibration spectra, in situ characterization, multi-scale simulations, review

中图分类号:

TQ115

陈昆峰, 胡乾宇, 刘锋, 薛冬峰. 多尺度晶体材料的原位表征技术与计算模拟研究进展[J]. 无机材料学报, 2023, 38(3): 256-269.

CHEN Kunfeng, HU Qianyu, LIU Feng, XUE Dongfeng. Multi-scale Crystallization Materials: Advances in in-situ Characterization Techniques and Computational Simulations[J]. Journal of Inorganic Materials, 2023, 38(3): 256-269.

图/表 14

图1 基于结晶生长的化学键合理论的快速晶体提拉生长技术制备的Ce: LYSO闪烁晶体的照片[9]

Fig. 1 Picture of Ce: LYSO scintillation crystals prepared by innovative fast crystal lifting growth technique based on the chemical bonding theory of crystalline growth[9]

图2 KDP生长结晶的原位光学显微镜照片

Fig. 2 In-situ optical microscope crystallization images of KDP growth (a) By optical microscope; (b) By camera

图3 水合碳酸钙结晶的红外光谱[31]

Fig. 3 Crystallization spectrum of hydrated calcium carbonate[31] (a) FTIR spectra of calcium carbonate monohydrate (blue spectrum) and aCCM (black spectrum); (b) Schematic representation of different vibrational modes; (c) FTIR spectra of aCCM recrystallized in acetonitrile mixture after different intervals; (d) Rectangle area in (c) showing hydroxyl stretching band region during recrystallization of aCCM after different intervals

图4 LCB晶体生长拉曼光谱及微观结构演变

Fig. 4 Raman spectra and microstructure evolution of LCB crystal growth (a) Raman spectra of LCB crystals at room temperature and LCB-grown raw material powders at different temperatures; (b, c) Molecular structural evolution in LCB-grown melts[38]

图5 三聚氰胺在不同压力下的能量色散X射线衍射图[55]

Fig. 5 Energy dispersive X-ray diffraction patterns of melamine at different pressures[55]

图6 ACC结晶时间分辨的WAXS及SAXS数据图[65]

Fig. 6 Time-resolved WAXS and SAXS data of ACC crystallization[65] (a) 3D representation of the time resolved WAXS modes in the experiment; (b) Stacked time series of the SAXS modes with time in minutes, and the arrows indicating positions of peaks caused by scattering from the growing vaterite crystallites, 1 Å =0.1 nm

图7 基于人工智能预测生长炉内状态, 平均预测时间可达到0.0003 s[78]

Fig. 7 Based on artificial intelligence, the average prediction time of the growth furnace state reaching 0.0003 s[78]

图8 材料在时间和空间两个维度上的多尺度计算方法[79]

Fig. 8 Time and space dimensions of multi-scale calculation method for materials[79]

图9 铌酸锂结构、缺陷、不同点缺陷的形成能与费米能的函数关系[82]

Fig. 9 Lithium niobate structure, defects and formation energy of different point defects as a function of Fermi energy[82] (a, b) Crystallographic structures of stoichiometric LN and congruent LN with anti-site NbLi4+ and VLi− defects. Green octahedra indicate NbO6 and LiO6; (c) Formation energies of different point defects (NbLi4+, VLi−, and NbLi4+ + VNb5−) in LN as a function of Fermi energy

图10 CaH6的原子结构(左)和电子局域函数(右)[83]

Fig. 10 Atomic structure (left) and electron localization functions (right) of CaH6[83]

图11 在能量最小化过程中, 界面层中原子的位移[92]

Fig. 11 Displacement of atoms in the interface layer during energy minimization[92] (a) Al atoms; (b) Ni atoms

图12 晶体生长速度V⊥随温度的变化[93]

Fig. 12 Variation of crystal growth rate V⊥with temperature[93] Error bars represent the standard deviation of growth velocity measurements. The system can still maintain the maximum growth rate in deep and supercooled region without temperature change

图13 通过路径搜索方法研究C60二聚体结合的最佳路线[94]

Fig. 13 Structural snapshots and relationship between energy and GSW of the optimal route for C60 dimer binding by the path search method[94] (a-f) Structural snapshots and relationship between energy and GSW steps; (g) GSW in the 0, 5, 15, 25, 40, and 80 GSW steps. Red and green represent two carbon atoms in C60, respectively

图14 在0.1 K/s的冷却速度下, Al-1% Cu合金凝固的相场模拟[97]

Fig. 14 Phase-field simulation of the solidification of Al-1% Cu alloy at a cooling rate of 0.1 K/s[97] (a1-a4) Solid fraction of the 2D system at 0.9; (b1-b4) Substantial fraction of the 3D system at 0.2; (b1-b4)3D systems corresponding to (a1-a4), respectively, containing different numbers of grains

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多尺度晶体材料的原位表征技术与计算模拟研究进展

Multi-scale Crystallization Materials: Advances in in-situ Characterization Techniques and Computational Simulations

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