Effect of macroscopic velocity on accommodation coefficients based on the molecular dynamics method

Zhang Ye; Zhang Ran; Lai Jian-Qi; Li Hua

doi:10.7498/aps.68.20190987

Abstract

Authors and contacts

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1. Introduction
2. Description of experiment
3. Results and analysis of the experiment
4. 损伤机理与模拟仿真
5. 结　论

References

Abstract

The accommodation coefficient is an important parameter in the field of rarefied gas dynamics, representing the adaptation level of the momentum and energy between gas molecules and solid surfaces, which is frequently used in the boundary conditions of numerical simulation of rarefied gas flow. In this paper, a physical model of the interaction between a single gas molecule Ar and the metal Pt surface is constructed, which greatly saves computational resources by omitting the collision between gas molecules in the bulk flow. The Pt surface is constructed by the Phontom model to reflect real physical properties. The surface roughness is modeled by a typical pyramid model, which is physically realistic and stable in structure. The molecular dynamics method is used to simulate the collision process of the gas molecules on the solid surface. The velocity sampling method is used so that the incident gas molecules possess the characteristics of macroscopic velocity. According to the average momentum and energy of the gas molecules before incidence and after reflection, the tangential momentum accommodation coefficients (TMACs), the normal momentum accommodation coefficients (NMACs) and the energy accommodation coefficients (EACs) are obtained. Moreover, the variation regularities of the accommodation coefficients with the macroscopic tangential velocity and the macroscopic normal velocity are analyzed under the smooth and rough surface, respectively. The results indicate that the accommodation coefficients have a strong correlation to macroscopic velocity and roughness. The increase of tangential velocity shortens the interaction time of gas molecules with solid surface, which results in the decrease of TMAC, NMAC and EAC of gas molecules, indicating that the tangential velocity has a negative effect on gas-solid adaptation. In addition, the momentum is found to be transformed from the tangential direction to the normal direction at a large tangential velocity, and this transformation tendency becomes more apparent as the tangential velocity increases. However, the influence of normal velocity on the momentum and energy accommodation coefficient is different. With regard to the smooth surface, the increase of normal velocity contributes to the tangential momentum and energy adaptation of gas molecules with the surface. While for the rough surface, the adaptation level of tangential momentum and energy between gas molecules and the surface is weakened as the normal velocity increases. This paper reveals the mechanism of gas molecules scattering on the surface from a microscopic point of view, which is quite different from the macroscopic phenomena. The conclusions of this paper indicate the irrationality of traditional scattering kernel models to represent the degree of accommodation through constant values, which is of great significance for the improvement of boundary conditions in rarefied gas flows.

Keywords:

PACS:

85.30.Tv	(Field effect devices)
81.05.U-	(Carbon/carbon-based materials)
94.20.Ss	(Electric fields; current system)
72.20.Jv	(Charge carriers: generation, recombination, lifetime, and trapping)

Authors and contacts

1.
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
2.
State Key Laboratory of Laser Interaction with Matter, Northwest Institute of Nuclear Technology, Xi’an 710024, China

Corresponding author: Zhang Ran, zr07024221@126.com

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 11472004)

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1. Introduction

Since its invention, graphene has attracted great attention for its excellent properties such as high mobility, high thermal conductivity and high mechanical strength. ^{[ 1 – 3 ]} Because of its excellent performance, graphene field effect transistors (GFETs) have great potential applications in devices and circuits, so some theoretical and practical problems about GFETs have been studied. ^{[ 4 , 5 ]} Among them, the reliability of GFET is a research focus. Graphene material is a single atomic layer with a very high specific surface area, and the charges are all on its surface, which is very vulnerable to the external environment. ^{[ 6 , 7 ]} The most striking manifestation is the Dirac voltage ( V _Dirac ) movement, V _Dirac Represents the voltage value at the Dirac point where the conduction and valence bands of graphene meet, when V _G ＞ V _Dirac The carriers in graphene are electrons when V _G ＜ V _Dirac The carriers in graphene are holes. Therefore V _Dirac The position of the GFET is one of the most important information, so what factors affect the GFET? V _Dirac Mobile has always been a matter of great concern. ^{[ 8 ]} .

In recent years, researchers have conducted experimental studies on the reliability of GFETs. 2014, Feng et al. ^{[ 9 ]} The effects of temperature and gate scanning range on the electrical characteristics of GFET with back gate structure are studied. It is found that the Dirac voltage of graphene V _Dirac With the increase of temperature, the hysteresis curve of GFET gradually moves from 0 V to 0 V. With the increase of temperature and gate voltage scanning range, the hysteresis curve of GFET is similar to that of 0 V. V _Dirac They attributed the hysteresis to the combined effects of temperature and gate voltage, the generation of interface trap charges in GFET with back-gate structure, and the redox reaction on the surface of graphene/oxide layer and the displacement of ions in the oxide layer. 2017, Zhang et al. ^{[ 10 ]} The dependence of buried-gate GFET transfer characteristics on gate voltage is studied. The experimental results show that the continuous positive or negative scanning of gate voltage will increase the voltage at the Dirac point when testing the transfer characteristics of GFET. Before testing the transfer characteristics of GFET, the increase of the holding time of gate voltage will gradually shift the Dirac voltage to the positive direction. It is pointed out that the magnitude of Dirac voltage is related to the magnitude of gate V..

To sum up, the reliability study of GFET is insufficient for the electrical stress experiment of GFET with top gate structure under different bias voltages. Therefore, the electrical stress experiment of GFET with top gate structure under different bias voltages is carried out in this paper to study the degradation law of electrical performance of GFET. On this basis, the damage mechanism is studied by TCAD numerical simulation.

2. Description of experiment

The research object is the graphene field effect transistor with top gate structure. The device structure such as Fig. 1 The graphene size of the GFET is 100 μm × 100 μm, and the gate oxide is 20 nm thick Al ₂ O ₃ , the substrate is composed of p-type Si and 90 nm thick SiO ₂ Single-layer graphene was grown and transferred by chemical vapor deposition (CVD) and polymer-assisted transfer.

$图 1 器件结构示意图\r\nFig. 1. Device structure diagram.$

图 1 器件结构示意图

Fig. 1. Device structure diagram.

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The electrical stress experiment and electrical parameter test were carried out by B1500 semiconductor analyzer. The transfer characteristic curve of GFET was tested for many times. The conditions of transfer characteristic curve test were as follows: gate voltage V _G Scan range – 5 — + 5 V, source voltage V _S Ground, drain voltage V _D Gradually increase from 20 mV. For example, Fig. 2 As shown, as the drain voltage is gradually increased, the GFET V _Dirac Gradually drift to the positive direction, which indicates that the hole doping gradually increases, the P-type doping gradually increases, and the N-type doping gradually decreases ^{[ 11 , 12 ]} The current of the GFET increases gradually, because the increasing voltage generates a stronger and stronger electric field, and the enhanced electric field gives enough energy to the electrons, so that the electrons can break away from the graphene and escape from the graphene, resulting in enhanced hole doping in the device, increased carrier concentration, and increased current. ^{[ 13 , 14 ]} The increased current will cause the temperature of graphene to rise, the vibration of the lattice to become more and more intense, and the number of escaping electrons to increase. ^{[ 15 , 16 ]} As a result, the hole doping of graphene is enhanced, the carrier concentration is increased, and the current is increased.

$图 2 GFET的转移特性曲线在不同漏极电压下的变化趋势\r\nFig. 2. The variations of the transfer characteristic curve of GFET under different drain voltages.$

图 2 GFET的转移特性曲线在不同漏极电压下的变化趋势

Fig. 2. The variations of the transfer characteristic curve of GFET under different drain voltages.

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The bias voltage conditions of the electric stress experiment are divided into three groups, namely, the grid electric stress ( V _G = –10 V, V _D = 0 V, V _S = 0 V), drain electrical stress ( V _G = 0 V, V _D = –10 V, V _S = 0 V), simultaneous gate and drain voltage applied electrical stress ( V _G = –10 V, V _D = –10 V, V _S = 0 V). The total duration of the electric stress test is 1000 s, and the electric stress period includes 5 test points. When the cumulative time of the electric stress reaches the test point, the semiconductor analyzer will automatically test the transfer characteristic curve of the GFET once under the set transfer characteristic curve test conditions, and save the data. The test conditions of the electrical stress test are as follows: Table 1 Shown.

表 1 电应力实验测试条件

Table 1. Electrical stress test conditions.

偏置电压条件	电应力测试时间点	转移特性曲线测试条件
栅极电应力(V_G = –10 V, V_D = 0 V, V_S = 0 V)	0 s, 100 s, 300 s, 500 s, 1000 s	V_G 从–5 V扫到5 V, V_D 为20 mV, V_S 接地
漏极电应力 (V_G = 0 V, V_D = –10 V, V_S = 0 V)
栅极电压与漏极电压同时施加的电应力(V_D = –10 V, V_D = –10 V, V_S = 0 V)

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| 显示表格

3. Results and analysis of the experiment

Fig. 3 — Fig. 5 The variation trend of the transfer characteristic curve of the GFET with the electrical stress accumulation time to 1000 s under three different bias voltages. Fig. 3 As shown, a GFET is electrically stress at that gate ( V _G = –10 V, V _D = 0 V, V _S = 0 V), the transfer characteristic curve of the device gradually moves to the negative direction with the gradual accumulation of electrical stress time. V _Dirac Drift in the negative direction, – 2 V before the electrical stress test, – 4.46 V after the electrical stress test, a change of 2.46 V.

$图 3 栅极电应力下, GFET的转移特性曲线随电应力累积时间的变化趋势\r\nFig. 3. Variations of GFET transfer characteristic curve with the accumulation time of electrical stress under gate voltage bias condition.$

图 3 栅极电应力下, GFET的转移特性曲线随电应力累积时间的变化趋势

Fig. 3. Variations of GFET transfer characteristic curve with the accumulation time of electrical stress under gate voltage bias condition.

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$图 5 栅极和漏极电压同时施加的电应力下, GFET转移特性曲线随电应力累积时间的变化趋势\r\nFig. 5. Variations of GFET transfer characteristic curve with the accumulation time of electrical stress under the condition of electrical stress applied by both the gate and drain voltages.$

图 5 栅极和漏极电压同时施加的电应力下, GFET转移特性曲线随电应力累积时间的变化趋势

Fig. 5. Variations of GFET transfer characteristic curve with the accumulation time of electrical stress under the condition of electrical stress applied by both the gate and drain voltages.

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$图 4 漏极电应力下, GFET的转移特性曲线随电应力累积时间的变化趋势\r\nFig. 4. Variations of GFET transfer characteristic curves with the accumulation time of electrical stress under drain voltage bias conditions.$

图 4 漏极电应力下, GFET的转移特性曲线随电应力累积时间的变化趋势

Fig. 4. Variations of GFET transfer characteristic curves with the accumulation time of electrical stress under drain voltage bias conditions.

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Such as Fig. 4 As shown, that electrical stress at the drain of the GFET ( V _G = 0 V, V _D = –10 V, V _S = 0 V), the transfer characteristic curve gradually moves to the positive direction. V _Dirac Drift in the positive direction, – 2.48 V before electrical stress test and – 1.94 V after electrical stress test, a change of 0.54 V. GFET device V _Dirac The drift direction is opposite to that of the gate electrical stress. The reason may be the difference of the types of trapped charges produced by the electrical stress under different voltage biases.

如图5所示, GFET在栅极和漏极电压同时施加的电应力(V_G = –10 V, V_D = –10 V, V_S = 0 V)作用下, 转移特性曲线逐渐向负方向移动. V_Dirac向负方向漂移, 电应力测试前为–2.5 V, 电应力测试后为–3.68 V, 变化量为1.18 V, 对比发现变化量明显要小于栅极电应力条件下的变化量.

图6给出了GFET的V_Dirac在两种不同的偏置电压条件下(栅极电应力、栅极和漏极电压同时施加的电应力)随电应力累积时间的变化趋势. 对比GFET在两种不同的偏置电压条件下V_Dirac的变化量, 发现栅极电应力诱导器件V_Dirac的退化趋势更加明显. 这可能是因为在栅极和漏极电压同时施加的电应力实验中产生的两种带电类型的陷阱电荷, 引起V_Dirac的漂移, 产生相互抵消的作用, 导致V_Dirac退化程度减小.

$图 6 VDirac随着电应力累积时间的变化趋势\r\nFig. 6. The variations of VDirac with the accumulation time of electrical stress.$

图 6 V_Dirac随着电应力累积时间的变化趋势

Fig. 6. The variations of V_Dirac with the accumulation time of electrical stress.

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计算GFET在不同偏置电压条件下的电应力实验中, 每个时间测试点对应的载流子迁移率. GFET的跨导G_m与载流子迁移率的关系如(1)式所示^[17,18]:

$\mu = \frac{L}{W}\cdot\frac{{G}_{{\mathrm{m}}}}{{C}_{{\mathrm{G}}}\cdot{V}_{{\mathrm{D}}}},$

(1)

其中, μ为载流子迁移率, L/W为长宽比, C_G为栅介质的电容, V_D为漏极电压, 跨导G_m为转移特性曲线的斜率. 详细计算参数L/W长宽比为1, C_G栅介质电容为3.54×10^–7 F/cm², V_D漏极电压为20 mV. 如图7所示, 不同偏置电压条件下的电应力实验中, GFET的空穴迁移率和电子迁移率均随着电应力累积时间的延长而逐渐降低. 载流子迁移率与氧化物陷阱电荷之间的关系为^[19]:

$图 7 载流子迁移率随电应力累积时间的变化趋势　(a)空穴迁移率随电应力累积时间的变化趋势; (b)电子迁移率随电应力累积时间的变化趋势\r\nFig. 7. The variations of carrier mobility with the accumulation time of electrical stress: (a) The variations of hole mobility with the accumulation time of electrical stress; (b) the variations of electron mobility with the accumulation time of electrical stress.$

图 7 载流子迁移率随电应力累积时间的变化趋势　(a)空穴迁移率随电应力累积时间的变化趋势; (b)电子迁移率随电应力累积时间的变化趋势

Fig. 7. The variations of carrier mobility with the accumulation time of electrical stress: (a) The variations of hole mobility with the accumulation time of electrical stress; (b) the variations of electron mobility with the accumulation time of electrical stress.

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$\frac{1}{\mu } = \frac{1}{{\mu }_{{\mathrm{c}}}\left({N}_{{\mathrm{o}}{\mathrm{t}}}{,Q}_{{\mathrm{g}}{\mathrm{c}}}\right)}+\frac{1}{{\mu }_{{\mathrm{S}}{\mathrm{R}}}}\left({E}_{{\mathrm{e}}{\mathrm{f}}{\mathrm{f}}}\right),$

(2)

$\mu _{\text{c}}^{{{ - 1}}} = \alpha \left( {{N_{{\text{ot}}}}/{N_0}} \right){\left( {\frac{1}{{1 + {Q_{{\text{gc}}}}/{Q_0}}}} \right)^\beta },$

(3)

其中, μ_c为受库仑散射作用的载流子迁移率, N_ot为氧化物陷阱电荷, μ_SR为受短程散射影响的载流子迁移率, Q_gc为电荷密度, E_eff为有效电场强度, α, β, N₀, Q₀为拟合参数. 由(2)式和(3)式可以得出, 载流子迁移率的退化是由于在电应力实验中产生了氧化物陷阱电荷, 增加了散射位点, 导致库仑散射作用增强, 载流子迁移率降低^[17–20].

载流子迁移率与界面陷阱电荷间的关系为^[21,22]:

$\mu = \frac{{\mu }_{0}}{1+\alpha \Delta {N}_{{\mathrm{i}}{\mathrm{t}}}},$

(4)

其中μ为实验后的载流子迁移率, $\Delta {N}_{{\mathrm{i}}{\mathrm{t}}}$ 为新产生的界面陷阱电荷, μ₀为实验前的载流子迁移率. 由(4)式得出, 新产生的界面陷阱电荷导致载流子迁移率的降低.

GFET在不同偏置电压条件下的电应力实验中, 产生了氧化物陷阱电荷和界面陷阱电荷, 增加了散射位点, 其库仑散射增强. 导致GFET的电子迁移率和空穴迁移率均随着电应力累积时间的延长而逐渐降低.

4. 损伤机理与模拟仿真

对于栅极电应力, 栅极电压为负, 电场方向垂直于沟道并指向栅极, 使得石墨烯沟道中的空穴浓度增大, 同时在负栅压的作用下, 空穴会隧穿经过石墨烯/栅极氧化层的界面并进入栅极氧化层中, 被材料里的缺陷俘获, 形成带正电荷的氧化物陷阱电荷. 并且沟道中的空穴受电场的影响会累积在石墨烯/栅极氧化层的界面处, 导致界面陷阱更容易俘获空穴, 形成带正电荷的界面陷阱电荷. 产生的陷阱电荷的带电类型一致, 对器件产生叠加效果, 导致GFET的V_Dirac向负方向漂移量更大, 退化程度更加明显^[23–25].

对于漏极电应力, 漏极一端电压为负, 存在由沟道指向漏极的电场方向, 使得石墨烯沟道中的电子浓度增大, 导致石墨烯/氧化层界面处的陷阱更容易俘获电子^[10,24], 形成带负电荷的界面陷阱电荷, 导致GFET的V_Dirac向正方向移动. 栅极电应力与漏极电应力产生的陷阱电荷带电类型不同, 因此造成GFET的V_Dirac漂移方向相反.

对于栅极和漏极电压同时施加的电应力, 在器件中既有垂直于沟道并指向栅极的电场, 也存在由沟道指向漏极的电场. 使得器件沟道中的空穴浓度和电子浓度均有所增大, 同时在电场的作用下, 空穴会隧穿进入栅极氧化层中形成带正电荷的氧化物陷阱电荷, 在石墨烯/栅极氧化层的界面形成带正电荷的界面陷阱电荷. 电子更容易被石墨烯/氧化层界面处的陷阱俘获, 形成带负电荷的界面陷阱电荷. 因此产生的陷阱电荷所带电类型不同, 对GFET的V_Dirac的漂移具有相互抵消的效果. 所以在栅极和漏极电压同时施加的电应力作用下, GFET的V_Dirac的漂移量小于栅极电应力条件下的漂移量, 退化程度减小.

为了更好地解释陷阱电荷带电类型的差异会对顶栅GFET器件V_Dirac的漂移方向和退化程度产生不同的影响. 结合TCAD仿真工具研究陷阱电荷对GFET的影响, 在TCAD仿真工具中建立顶栅GFET器件的二维结构模型, 石墨烯沟道长度为100 μm, 石墨烯材料则是通过修改多晶硅的材料参数完成代替^[26,27]. 仿真过程中, 在GFET的石墨烯/氧化层界面处加入带正电荷的固定陷阱电荷和带负电荷的固定陷阱电荷, 用来模拟在不同偏置电压条件下, 电应力产生陷阱电荷带电类型的差异对GFET的V_Dirac退化影响.

图8为TCAD仿真结果, 给出了带不同电性的固定陷阱电荷对GFET的转移特性曲线的影响. 如图8(a)所示, 随着带正电荷的固定陷阱电荷浓度的升高, 器件的转移特性曲线逐渐向负向移动, V_Dirac也逐渐向负方向漂移. 如图8(b)所示, 随着带负电荷的固定陷阱电荷浓度的升高, 器件的转移特性曲线逐渐向正方向移动, V_Dirac也逐渐向正方向漂移. 对比TCAD仿真结果, 证明陷阱电荷带电类型的差异是导致GFET的V_Dirac向不同的方向漂移的原因. 为了验证带不同电性的陷阱电荷会对GFET的V_Dirac漂移量有抵消的效果, 图8(c)中最右边的黑色转移特性曲线未添加固定陷阱电荷, 其余的转移特性曲线添加了相同浓度的带正电荷的固定陷阱电荷. 随着带负电荷的固定陷阱电荷浓度从0 cm^–2逐渐增大, 图中的转移特性曲线逐渐向正方向移动, 逐渐靠近未添加固定陷阱电荷的转移特性曲线. 通过TCAD仿真结果发现, 在带正电荷的固定陷阱电荷和带负电荷的固定陷阱电荷共同作用下, GFET的V_Dirac漂移量逐渐减小. 证明带不同电性的陷阱电荷对GFET的V_Dirac漂移量会产生抵消作用.

$图 8 TCAD仿真结果　(a)带正电荷的固定陷阱电荷对GFET转移特性曲线的影响; (b)带负电荷的固定陷阱电荷对GFET转移特性曲线的影响; (c)带正电荷和带负电荷的固定陷阱电荷同时对GFET转移特性曲线的影响\r\nFig. 8. TCAD simulation results: (a) Effect of a positively charged fixed trap charge on the transfer characteristic curve of GFET device; (b) effect of negatively charged fixed trap charge on the transfer characteristic curve of GFET device; (c) the effect of both positively charged and negatively charged fixed trap charges on the transfer characteristic curve of GFET devices.$

图 8 TCAD仿真结果　(a)带正电荷的固定陷阱电荷对GFET转移特性曲线的影响; (b)带负电荷的固定陷阱电荷对GFET转移特性曲线的影响; (c)带正电荷和带负电荷的固定陷阱电荷同时对GFET转移特性曲线的影响

Fig. 8. TCAD simulation results: (a) Effect of a positively charged fixed trap charge on the transfer characteristic curve of GFET device; (b) effect of negatively charged fixed trap charge on the transfer characteristic curve of GFET device; (c) the effect of both positively charged and negatively charged fixed trap charges on the transfer characteristic curve of GFET devices.

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5. 结　论

本文研究了顶栅结构的GFET器件在不同的电应力条件作用下, 器件电学性能的退化规律. 发现GFET的载流子迁移率随着电应力累积时间的延长不断退化, 但不同偏置电压条件的电应力作用下, GFET的V_Dirac的漂移方向和退化程度不同. 分析认为, 不同偏置电压条件的电应力作用会在GFET中产生不同方向的电场, 影响沟道中载流子浓度和分布. 在电场方向的作用下, 空穴和电子隧穿进入氧化层, 被材料中的缺陷俘获, 形成氧化物陷阱电荷, 并且还会促使石墨烯/氧化层界面处的陷阱俘获空穴和电子, 形成界面陷阱电荷, 这是造成GFET的载流子迁移率和V_Dirac退化的主要原因. 同时产生的陷阱电荷所带电类型的差异, 是导致GFET的V_Dirac的漂移方向和退化程度不同的主要原因. 结合TCAD仿真模拟表明, 带正电荷的陷阱电荷导致GFET的V_Dirac向负方向漂移, 而带负电荷的陷阱电荷会导致GFET的V_Dirac向正方向漂移, 并且当两种陷阱电荷同时存在时, 会对GFET的V_Dirac漂移量产生抵消作用, 证明陷阱电荷的带电类型差异对GFET的V_Dirac产生不同的退化影响, 为GFET在实际应用中提供参考和帮助.

References

[1]	Verbridge S S, Craighead H G, Parpia J M 2008 Appl. Phys. Lett. 92 013112 Google Scholar
[2]	Zhang Z Q, Zhang H W, Ye H F 2009 Appl. Phys. Lett. 95 154101 Google Scholar
[3]	Song H Q, Yu M X, Zhu W Y, Zhang Y, Jiang S X 2013 Chin. Phys. Lett. 30 014701 Google Scholar
[4]	Rovenskaya O I 2015 Int. J. Heat Mass Tran. 89 1024 Google Scholar
[5]	Bao F B, Huang Y L, Zhang Y H, Lin J Z 2015 Microfluid Nanofluid 18 1075 Google Scholar
[6]	Cao B Y, Sun J, Chen M, Guo Z Y 2009 Int. J. Mol. Sci. 10 4638 Google Scholar
[7]	Bird G A 1994 Molecular Gas Dynamics and the Direct Simulation of Gas Flows (Oxford: Oxford University Press) pp199−206
[8]	Fan J, Shen C 2001 J. Comput. Phys. 167 393
[9]	Yuan Y D, Rahman S 2016 Physica A 463 25 Google Scholar
[10]	Verbeek M G 2018 Microfluid. Nanofluid. 22 34 Google Scholar
[11]	Maxwell J C 1879 Phil. Trans. R. Soc. Lond. 170 231 Google Scholar
[12]	Cercignani C, Lampis M 1971 Transp. Theory Stat. Phys. 1 101 Google Scholar
[13]	Lord R G 1991 Phys. Fluids 3 706 Google Scholar
[14]	Agrawal A, Prabhu S V 2008 J. Vac. Sci. Technol. A 26 634
[15]	Yakunchikov A N, Kovalev V L, Utyuzhnikov S V 2012 Chem. Phys. Lett. 554 225 Google Scholar
[16]	Zhang W, Meng G, Wei X 2012 Microfluid. Nanofluid. 13 845 Google Scholar
[17]	Zhang H W, Zhang Z Q, Zheng Y G, Ye H F 2010 Phys. Rev. E 81 066303 Google Scholar
[18]	Rapaport D C 2004 The Art of Molecular Dynamics Simulation (New York: Cambridge University Press) pp4−5
[19]	Bao F B, Huang Y L, Qiu L M, Lin J Z 2015 Mol. Phys. 113 561 Google Scholar
[20]	Spijker P, Markvoort A J, Nedea S V, Hilbers P A 2010 Phys. Rev. E 81 011203 Google Scholar
[21]	Bruno D, Cacciatore M, Longo S, Rutigliano M 2000 Chem. Phys. Lett. 320 245 Google Scholar
[22]	Kovalev V, Yakunchikov A, Li F 2011 Acta Astronaut. 69 744 Google Scholar
[23]	Dongari N, Zhang Y H, Reese J M 2012 AIP Conf. Proc. 1501 895
[24]	Cao B Y, Chen M, Guo Z Y 2005 Appl. Phys. Lett. 86 091905 Google Scholar
[25]	Finger G W, Kapat J S, Bhattacharya A 2007 J. Fluids Eng. 129 31
[26]	Sun J, Li Z X 2008 Mol. Phys. 106 2325 Google Scholar
[27]	Sun J, Li Z X 2009 Mol. Simul. 35 228 Google Scholar
[28]	Sun J, Li Z X 2010 Comput. Fluids 39 1345 Google Scholar
[29]	Prabha S K, Sathian S P 2012 Comput. Fluids 68 47
[30]	Pham T T, To Q D, Lauriat G, Leonard C 2012 Phys. Rev. E 86 051201 Google Scholar
[31]	Liang Z, Keblinski P 2014 Int. J. Heat Mass Tran. 78 161 Google Scholar
[32]	Reinhold J, Veltzke T, Wells B, Schneider J, Meierhofer F, Colombi Ciacchi L, Chaffee A 2014 Comput. Fluids 97 31 Google Scholar
[33]	Lim W W, Suaning G J, McKenzie D R 2016 Phys. Fluids 28 097101 Google Scholar
[34]	Yamaguchi H, Matsuda Y, Niimi T 2017 Phys. Rev. E 96 013116 Google Scholar
[35]	张冉, 谢文佳, 常青, 李桦 2018 物理学报 67 084701 Google Scholar Zhang R, Xie W J, Chang Q, Li H 2018 Acta Phys. Sin. 67 084701 Google Scholar
[36]	Cao B Y, Chen M, Guo Z Y 2006 Int. J. Eng. Sci. 44 927 Google Scholar
[37]	Xie J F, Cao B Y 2016 Mol. Simul. 43 65
[38]	张冉, 常青, 李桦 2018 物理学报 67 223401 Google Scholar Zhang R, Chang Q, Li H 2018 Acta Phys. Sin. 67 223401 Google Scholar
[39]	Maruyama S 2000 Advances in Numerical Heat Transfer (Vol.2) (Boca Raton : CRC Press) pp189
[40]	张烨, 张冉, 常青, 李桦 2019 物理学报 68 124702 Google Scholar Zhang Y, Zhang R, Chang Q, Li H 2019 Acta Phys. Sin. 68 124702 Google Scholar
[41]	Wu L, Bogy D B 2002 J. Tribol.-T. ASME 124 562 Google Scholar

图 1 气体分子在固体表面散射的物理模型

Figure 1. Physical model for scattering gas molecules on solid surfaces.

DownLoad: Full-Size Img PowerPoint

图 2 表面粗糙度的纳米构型

Figure 2. Schematic diagram of the roughness geometry.

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图 3 不同宏观切向速度下的切向动量适应系数

Figure 3. TMAC values of gas molecular under different macroscopic tangential velocities.

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图 4 不同宏观切向速度下气体分子与表面作用时间

Figure 4. Time of gas-solid interaction under different macroscopic tangential velocities.

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图 5 不同宏观切向速度及法向速度下的切向动量适应系数

Figure 5. TMAC values of gas molecular under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 6 不同宏观切向速度及法向速度下的法向动量适应系数

Figure 6. NMAC values of gas molecular under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 7 不同宏观切向及法向速度下的法向能量适应系数

Figure 7. EAC values of gas molecular under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 8 粗糙度为0.5 A的粗糙表面下, 不同宏观切向速度及法向速度下的切向动量适应系数

Figure 8. TMAC values of gas molecular on rough surfaces of 0.5 A under different macroscopic tangential and normal velocities

DownLoad: Full-Size Img PowerPoint

图 9 粗糙度为0.5 A的粗糙表面下, 不同宏观切向速度及法向速度下的气体分子吸附概率

Figure 9. Sticking probability of gas molecular on rough surfaces of 0.5 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 10 粗糙度为0.5 A的粗糙表面下, 不同宏观切向速度及法向速度下的法向动量适应系数

Figure 10. NMAC values of gas molecular on rough surfaces of 0.5 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 11 粗糙度为0.5 A的粗糙表面下, 不同宏观切向速度及法向速度下的能量适应系数

Figure 11. EAC values of gas molecular on rough surfaces of 0.5 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 12 粗糙度为1.0 A的粗糙表面下, 不同宏观切向速度及法向速度下的切向动量适应系数

Figure 12. TMAC values of gas molecular on rough surfaces of 1.0 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 13 粗糙度为1.0 A的粗糙表面下, 不同宏观切向速度及法向速度下的气体分子吸附概率

Figure 13. Sticking probability of gas molecular on rough surfaces of 1.0 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 14 粗糙度为1.0 A的粗糙表面下, 不同宏观切向速度及法向速度下的法向动量适应系数

Figure 14. NMAC values of gas molecular on rough surfaces of 1.0 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

图 15 粗糙度为1.0 A的粗糙表面下, 不同宏观切向速度及法向速度下的能量适应系数

Figure 15. EAC values of gas molecular on rough surfaces of 1.0 A under different macroscopic tangential and normal velocities.

DownLoad: Full-Size Img PowerPoint

表 1 分子动力学模拟中的参数

Table 1. Parametersin the MD simulations.

相互作用	$\sigma {\rm{/nm}}$	$\varepsilon {\rm{/J}}$	mass/kg
Ar-Ar	0.3405	$1.670 \times {10^{-{\rm{21}}}}$	$6.633 \times {10^{-{\rm{26}}}}$
Pt-Pt	0.2770	$52.07 \times {10^{ -{\rm{21}}}}$	$32.36 \times {10^{-{\rm{26}}}}$
Ar-Pt	0.3085	$0.894 \times {10^{ - {\rm{21}}}}$	—

DownLoad: CSV

[1]	Verbridge S S, Craighead H G, Parpia J M 2008 Appl. Phys. Lett. 92 013112 Google Scholar
[2]	Zhang Z Q, Zhang H W, Ye H F 2009 Appl. Phys. Lett. 95 154101 Google Scholar
[3]	Song H Q, Yu M X, Zhu W Y, Zhang Y, Jiang S X 2013 Chin. Phys. Lett. 30 014701 Google Scholar
[4]	Rovenskaya O I 2015 Int. J. Heat Mass Tran. 89 1024 Google Scholar
[5]	Bao F B, Huang Y L, Zhang Y H, Lin J Z 2015 Microfluid Nanofluid 18 1075 Google Scholar
[6]	Cao B Y, Sun J, Chen M, Guo Z Y 2009 Int. J. Mol. Sci. 10 4638 Google Scholar
[7]	Bird G A 1994 Molecular Gas Dynamics and the Direct Simulation of Gas Flows (Oxford: Oxford University Press) pp199−206
[8]	Fan J, Shen C 2001 J. Comput. Phys. 167 393
[9]	Yuan Y D, Rahman S 2016 Physica A 463 25 Google Scholar
[10]	Verbeek M G 2018 Microfluid. Nanofluid. 22 34 Google Scholar
[11]	Maxwell J C 1879 Phil. Trans. R. Soc. Lond. 170 231 Google Scholar
[12]	Cercignani C, Lampis M 1971 Transp. Theory Stat. Phys. 1 101 Google Scholar
[13]	Lord R G 1991 Phys. Fluids 3 706 Google Scholar
[14]	Agrawal A, Prabhu S V 2008 J. Vac. Sci. Technol. A 26 634
[15]	Yakunchikov A N, Kovalev V L, Utyuzhnikov S V 2012 Chem. Phys. Lett. 554 225 Google Scholar
[16]	Zhang W, Meng G, Wei X 2012 Microfluid. Nanofluid. 13 845 Google Scholar
[17]	Zhang H W, Zhang Z Q, Zheng Y G, Ye H F 2010 Phys. Rev. E 81 066303 Google Scholar
[18]	Rapaport D C 2004 The Art of Molecular Dynamics Simulation (New York: Cambridge University Press) pp4−5
[19]	Bao F B, Huang Y L, Qiu L M, Lin J Z 2015 Mol. Phys. 113 561 Google Scholar
[20]	Spijker P, Markvoort A J, Nedea S V, Hilbers P A 2010 Phys. Rev. E 81 011203 Google Scholar
[21]	Bruno D, Cacciatore M, Longo S, Rutigliano M 2000 Chem. Phys. Lett. 320 245 Google Scholar
[22]	Kovalev V, Yakunchikov A, Li F 2011 Acta Astronaut. 69 744 Google Scholar
[23]	Dongari N, Zhang Y H, Reese J M 2012 AIP Conf. Proc. 1501 895
[24]	Cao B Y, Chen M, Guo Z Y 2005 Appl. Phys. Lett. 86 091905 Google Scholar
[25]	Finger G W, Kapat J S, Bhattacharya A 2007 J. Fluids Eng. 129 31
[26]	Sun J, Li Z X 2008 Mol. Phys. 106 2325 Google Scholar
[27]	Sun J, Li Z X 2009 Mol. Simul. 35 228 Google Scholar
[28]	Sun J, Li Z X 2010 Comput. Fluids 39 1345 Google Scholar
[29]	Prabha S K, Sathian S P 2012 Comput. Fluids 68 47
[30]	Pham T T, To Q D, Lauriat G, Leonard C 2012 Phys. Rev. E 86 051201 Google Scholar
[31]	Liang Z, Keblinski P 2014 Int. J. Heat Mass Tran. 78 161 Google Scholar
[32]	Reinhold J, Veltzke T, Wells B, Schneider J, Meierhofer F, Colombi Ciacchi L, Chaffee A 2014 Comput. Fluids 97 31 Google Scholar
[33]	Lim W W, Suaning G J, McKenzie D R 2016 Phys. Fluids 28 097101 Google Scholar
[34]	Yamaguchi H, Matsuda Y, Niimi T 2017 Phys. Rev. E 96 013116 Google Scholar
[35]	张冉, 谢文佳, 常青, 李桦 2018 物理学报 67 084701 Google Scholar Zhang R, Xie W J, Chang Q, Li H 2018 Acta Phys. Sin. 67 084701 Google Scholar
[36]	Cao B Y, Chen M, Guo Z Y 2006 Int. J. Eng. Sci. 44 927 Google Scholar
[37]	Xie J F, Cao B Y 2016 Mol. Simul. 43 65
[38]	张冉, 常青, 李桦 2018 物理学报 67 223401 Google Scholar Zhang R, Chang Q, Li H 2018 Acta Phys. Sin. 67 223401 Google Scholar
[39]	Maruyama S 2000 Advances in Numerical Heat Transfer (Vol.2) (Boca Raton : CRC Press) pp189
[40]	张烨, 张冉, 常青, 李桦 2019 物理学报 68 124702 Google Scholar Zhang Y, Zhang R, Chang Q, Li H 2019 Acta Phys. Sin. 68 124702 Google Scholar
[41]	Wu L, Bogy D B 2002 J. Tribol.-T. ASME 124 562 Google Scholar

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Vol. 68, Issue 22 - 2019-11-20

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