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The high-quality growth of GaAs crystals is extremely essential for the fabrication of high-performance high-frequency microwave electronic devices and light-emitting devices. In this work, the molecular dynamics (MD) simulation is used to simulate the induced crystallization of GaAs crystal along the [110] orientation. The effects of strain on the growth process and defect formation are analyzed by the largest standard cluster analysis, the pair distribution function, and visualization analysis. The results indicate that the crystallization process of GaAs crystal changes significantly under different strain conditions. At the initial stage, the crystal growth rate of the system decreases after a certain tensile strain and a large compressive strain have been applied, and the greater the strain, the lower the crystallization rate is. In addition, as the crystal grows, the system forms a zigzag interface bounded by the {111} facet, and the angle between the growth plane and the {111} facet affects the morphology of the solid-liquid interface and further affects the formation of twins. The larger the applied tensile strain and the smaller the angle, the more twin defects will form and the more irregular they will be. At the same time, a large proportion of the dislocations in the system is associated with twins. The application of strain can either inhibit or promote the nucleation of dislocations, and under an appropriate amount of strain size, crystals without dislocations can even grow. The study of the microstructural evolution of GaAs on an atomic scale provides a reference for crystal growth theory.
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Keywords:
- molecular dynamics /
- GaAs /
- induced crystallization /
- strain
[1] Santos Gomes B, Masia F 2022 Journal of Colloid and Interface Science 625 743
Google Scholar
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Google Scholar
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Google Scholar
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[10] Whelan J M, Wheatley G H 1958 J. Phys. Chem. Solids 6 169
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[39] Finney J L, Bernal J D 1997 Proc. Royal Society of London. A. Mathemat. Phys. Sci. 319 479
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图 1 MD模拟中GaAs晶体的固液模型. 蓝色原子代表晶体原子, 灰色原子代表熔体原子
Figure 1. Solid-liquid model of GaAs crystal in the MD simulation. The blue atoms represent the crystal atoms and the grey atoms represent the melting atoms.
图 2 (a) [4-S000] LaSC示意图; (b) 7种应变下体系的LaSC构型熵变化
Figure 2. (a) Diagram of [4-S000] LaSC; (b) LaSC entropy changes for the systems at seven strains.
图 3 不同应变下熔体区域的PDF曲线
Figure 3. PDF curves of the melt region at different strains.
图 4 施加不同应变时结晶速率随时间的变化, 插图显示0—0.7 ns时间内的放大部分
Figure 4. Variation of crystallization rates with time under different strains, and the inset shows the enlarged portion for the time range of 0 to 0.7 ns.
图 5 孪晶缺陷原子数(a)和位错密度(b)随时间的变化曲线
Figure 5. Variation of the number of twin defect atoms (a) and dislocation density with time (b).
图 6 不同应变下GaAs诱导式结晶体系的微观结构演变过程 (蓝色原子代表闪锌矿结构原子; 黄色原子代表纤锌矿结构原子; 白色原子代表其他类型的无序原子)
Figure 6. Microstructural evolution of GaAs-induced crystallization systems at different strains (Blue atoms represent zinc-blende structure atoms; yellow atoms represent wurtzite structure atoms; white atoms are other types of disordered atoms).
图 7 ε = –0.02时的ADF曲线
Figure 7. ADF curves at ε = –0.02.
图 8 (a) 对GaAs晶体的生长等温弛豫一段时间后系统的原子结构; (b) {111}面与生长平面之间的几何关系示意图
Figure 8. (a) Atomic structure of the system after a period of isothermal relaxation for the growth of GaAs crystals; (b) schematic diagram of the geometric relationship between the {111} plane and the growth plane.
图 9 压杆位错形成过程 (a) 100 ps; (b) 900 ps. 压杆位错形成图解 (c)位错反应前; (d)位错反应后
Figure 9. Formation process of Lomer-Cottrell dislocation: (a) 100 ps; (b) 900 ps. Formation diagram of Lomer-Cottrell dislocation: (c) Before dislocation reaction; (d) after dislocation reaction.
参数 Ga—Ga As—As Ga—As γ 0.007874 0.455 0.0166 S 1.11 1.86 1.1417 d 0.75 0.1612 0.56 β/Å–1 1.08 1.435 1.5228 D0/eV 1.40 3.96 2.10 R0/Å 2.3235 2.10 2.35 c 1.918 0.1186 1.29 h = cosθ0 0.3013 0.07748 0.237 α/Å–1 1.846 3.161 0 Rc/Å 2.95 3.40 3.10 D/Å 0.15 0.20 0.20 
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[1] Santos Gomes B, Masia F 2022 Journal of Colloid and Interface Science 625 743
Google Scholar
[2] 王鹏华, 唐吉龙, 亢玉彬, 方铉, 房丹, 王登魁, 林逢源, 王晓华, 魏志鹏 2019 物理学报 68 087803
Google Scholar
Wang P H, Tang J L, Kang Y B, Fang X, Fang D, Wang D K, Lin F Y, Wang X H, Wei Z P 2019 Acta Phys. Sin. 68 087803
Google Scholar
[3] Carter M A, Mottram A, Peaker A R, Sudlow P D, White T 1971 Nature 232 469
Google Scholar
[4] Chuang L C, Sedgwick F G, Chen R, Ko W S, Moewe M, Ng K W, Tran T T D, Chang-Hasnain C 2011 Nano Lett. 11 385
Google Scholar
[5] Currie M, Dianat P, Persano A, Martucci M C, Quaranta F, Cola A, Nabet B 2013 Sensors 13 2475
Google Scholar
[6] Ukita H, Uenishi Y, Tanaka H 1993 Science 260 786
Google Scholar
[7] Mangla O, Roy S 2020 Materials Letters 274 128036
Google Scholar
[8] Papež N, Dallaev R, Ţălu Ş, Kaštyl J 2021 Materials 14 3075
Google Scholar
[9] Ghalgaoui A, Reimann K, Woerner M, Elsaesser T, Flytzanis C, Biermann K 2018 Phys. Rev. Lett. 121 266602
Google Scholar
[10] Whelan J M, Wheatley G H 1958 J. Phys. Chem. Solids 6 169
Google Scholar
[11] Wu T, Wei J, Liu H, Ma S, Chen Y, Ren J 2021 Electronics 10 1482
Google Scholar
[12] Murakami M 2002 Sci. Technol. Adv. Mater. 3 1
Google Scholar
[13] Jiang P, Balram K C 2020 Opt. Express 28 12262
Google Scholar
[14] Zhan L, Xia F, Xia Y, Xie B 2018 ACS Sustainable Chem. Eng. 6 1336
Google Scholar
[15] Sosso G C, Chen J, Cox S J, Fitzner M, Pedevilla P, Zen A, Michaelides A 2016 Chem. Rev. 116 7078
Google Scholar
[16] Shibuta Y, Sakane S, Miyoshi E, Okita S, Takaki T, Ohno M 2017 Nat. Communica. 8 10
Google Scholar
[17] Jia T, Wang Z, Tang M, Xue Y, Huang G, Nie X, Lai S, Ma W, He B, Gou S 2022 Nanomaterials 12 611
Google Scholar
[18] Thu H T T, Hoang V V 2010 Computa. Mater. Sci. 49 S221
Google Scholar
[19] Luo J, Gao T, Ren L, Xie Q, Tian Z, Chen Q, Liang Y 2019 Mater. Sci. Semicon. Proc. 104 104680
Google Scholar
[20] Gulluoglu A N, Zhu X, Tsai C T 2001 J. Mater. Sci. 36 3557
Google Scholar
[21] Meduoye G O, Bacon D J, Evans K E 1988 J. Crystal Growth 88 397
Google Scholar
[22] Subramanyam N, Tsai C T 1995 J. Mater. Proc. Technol. 55 278
Google Scholar
[23] Zhu X A, Tsai C T 2004 Computa. Mater. Sci. 29 334
Google Scholar
[24] Gulluoglu A N, Tsai C T, Hartley C S, Chait A 1994 Model. Simul. Mater. Sci. Eng. 2 67
Google Scholar
[25] Pavia F, Curtin W A 2015 Model. Simul. Mater. Sci. Eng. 23 055002
Google Scholar
[26] Plimpton S 1995 J. Computa. Phys. 117 1
Google Scholar
[27] Albe K, Nordlund K, Nord J, Kuronen A 2002 Phys. Rev. B 66 035205
Google Scholar
[28] Fichthorn K A, Tiwary Y, Hammerschmidt T, Kratzer P, Scheffler M 2011 Phys. Rev. B 83 195328
Google Scholar
[29] 陈庆, 陈茜, 梁永超, 高廷红, 田泽安, 谢泉 2017 科学通报 62 1386
Google Scholar
Chen Q, Chen Q, Liang Y C, Gao T H, Tian Z A, Xie Q 2017 Chin. Sci. Bulletin 62 1386
Google Scholar
[30] Hoover W G 1985 Phys. Rev. A 31 1695
Google Scholar
[31] Tian Z A, Liu R S, Dong K J, Yu A B 2011 EPL 96 36001
Google Scholar
[32] Terban M W, Billinge S J L 2022 Chem. Rev. 122 1208
Google Scholar
[33] Tian Z A, Dong K J, Yu A B 2013 AIP Confer. Proc. 1542 373
Google Scholar
[34] 韦国翠, 田泽安 2021 物理学报 70 246401
Google Scholar
Wei G C, Tian Z A 2021 Acta Phys. Sin. 70 246401
Google Scholar
[35] Hu L, Tian Z, Liang Y, Gao T, Chen Q, Zheng Q, Luo Y, Xie Q 2022 J. Alloys Compounds 897 162743
Google Scholar
[36] Richert R, Angell C A 1998 J. Chem. Phys. 108 9016
Google Scholar
[37] Capaccioli S, Prevosto D, Lucchesi M, Amirkhani M, Rolla P 2009 J. Non-Crystalline Solids 355 753
Google Scholar
[38] Scott G D, Mader D L 1964 Nature 201 382
Google Scholar
[39] Finney J L, Bernal J D 1997 Proc. Royal Society of London. A. Mathemat. Phys. Sci. 319 479
Google Scholar
[40] Sheng H W, Luo W K, Alamgir F M, Bai J M, Ma E 2006 Nature 439 419
Google Scholar
[41] Stukowski A, Bulatov V V, Arsenlis A 2012 Model. Simul. Mater. Sci. Eng. 20 085007
Google Scholar
[42] Liu C S, Xia J, Zhu Z G, Sun D Y 2001 J. Chem. Phys. 114 7506
Google Scholar
[43] Stukowski A 2009 Model. Simul. Mater. Sci. Eng. 18 015012
Google Scholar
[44] Li D H, Moore R A, Wang S 1988 J. Chem. Phys. 89 4309
Google Scholar
[45] Mitchell T E, Unal O 1991 J. Electro. Mater. 20 723
Google Scholar
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