Advertisement

Nanomanufacturing and Metrology

, Volume 2, Issue 3, pp 160–167 | Cite as

The Simulation and Research of Etching Function Based on Scanning Electrochemical Microscopy

  • Xiaole Wang
  • Lianhuan Han
  • Yanquan Geng
  • Xuesen Zhao
  • Yongzhi Cao
  • Zhenjiang Hu
  • Dongping Zhan
  • Yongda YanEmail author
Original Articles
  • 67 Downloads

Abstract

Scanning electrochemical microscopy (SECM) has already been employed as a micromachining method for a long time. However, coupling effects of some factors, including the voltage of the tool electrode, the distance between the tool electrode and substrates, tip current, and machining time on the machining process, have not been clearly stated. In this study, based on simulation and experimental results, an etching function between the etching depth and the above influencing factors in the machining process with SECM is proposed. First, the influence of the tool electrode, the distance between the tool electrode and substrates, tip current, and machining time on the etching depth was analyzed by a two-dimensional (2D) axisymmetric finite element model. Second, the etching function between these factors and the etching depth was established. Finally, this relationship was then simplified and verified by etching experiments. In summary, the etching function can be used to guide the etching process to machine 2D and 3D microstructures with SECM.

Keywords

Etching function Etching depth Scanning electrochemical microscopy 

1 Introduction

The machining method of scanning electrochemical microscopy (SECM) was proposed by Bard et al. [1]. Nowadays, this method has been widely used in many fields, including electrocatalysis, corrosion, and elucidation of charge transfer kinetics and mechanisms of heterogeneous processes, biophysical systems, semiconductors or liquid–liquid and liquid–gas interfaces [2]. Among these methods, by combining this method with the piezoelectric element and other systems, the electrode is controlled to access substrates, and the oxidation–reduction reaction generates between the tip and substrates by increasing the voltage of the tool electrode to realize the etching results. The accuracy of etching results could be increased up to the nanometer level by improving parameters, including the size of the tool electrode and the motion control accuracy. This method has the significant advantages of high spatial resolution [3, 4, 5].

In addition to the factors of the reaction system such as the type of substrates and type of solution, the voltage of the tool electrode, the distance between the tool electrode and substrates, machining time, and tip current are all related to the etching results [6]. Mandler and Bard [7] held the tip at a constant distance near the surface and obtained the feedback current. For the analysis of the influence of these factors on etching results, simulations on a two-dimensional model are one of the most commonly used methods [8, 9, 10, 11, 12, 13]. The results would be consistent with the experimental results and could be traced and obtained quantitatively. Shimizu et al. [14] and Mirkin and Bard [15] applied a 2D integral equation to solve the diffusion problem for two quasi-reversible electron-transfer processes occurring between the tool electrode and substrates. For the multiphase reaction, the reaction rate was the extracted flux of the substance generated on substrates that was evaluated through the concentration of the generated substance by the reported the substrate-generation/tip-collection mode [16]. The equilibrium state and kinetic results were studied by introducing a diffusion coefficient [17]. However, the coupling effects of these factors have not been clearly stated. The aim of this article was to propose the etching function using gallium arsenide (GaAs) as an example. The etching function between the parameters of the multiphase reaction and the etching results was studied based on the 2D integral equation. This function enabled us to establish the relationship between machining depth and machining parameters, thus making parameter design more convenient. In theory, according to the relationship between tip current and tip voltage from the Nernst function [18, 19], and the relationship between etching depth and the tip current [20], we could determine the relationship between the etching depth and the tip voltage. By establishing a two-dimensional numerical simulation model by COMSOL software, we could understand the theoretical etching function. In addition to the voltage of the electrode, the etching time, and the tool electrode–substrate distance could be considered. We could also modify the etching function mentioned above by experiments.

2 Theory and Simulations

The etching processes of GaAs can be expressed as follows:
$$E :\quad 2{\text{Br}}^{ - } \to {\text{Br}}_{2} + 2{\text{e}}^{ - }$$
(1)
$$C:\quad 3{\text{Br}}_{2} + {\text{GaAs}} + 3{\text{H}}_{2} {\text{O}} \to {\text{Ga}}^{{_{3 + } }} + {\text{AsO}}_{3}^{3 - } + 6{\text{Br}}^{ - } + 6{\text{H}}^{ + } ,$$
(2)
where E is the oxidation reaction occurring at the surface of the tool electrode and C is the etching reaction. As the etching agent precursor, Br could not react with GaAs. When GaAs reacts with Br2, which is the product of E, the etching of GaAs is realized.
The 2D axisymmetric model is established as illustrated in Fig. 1. This model could be used for not only steady-state analysis but also any transient analysis. Boundaries 1, 4, and 5 represent insulation/symmetry boundary, and boundaries 6, 7, 2, 8, and 9 represent the concentration boundary and the flux boundary, respectively. The setting and simplification of the model are the same as those of the model reported previously [21]. Among them, the setting of boundary 3, where the formation of the etching agent formation reaction takes place, is slightly different. The concentration changes according to functions (3) and (4), where parameters and represent concentrations of Br and Br2 at a given moment, respectively. Parameter respects the initial concentration of Br, where the initial concentration of Br2 is 0, and the parameter k1 is the degree of etching reaction calculated by Nernst Eq. (5)
$$C_{{{\text{Br}}^{ - } }}^{\prime } = (1 - k_{1} )C_{{{\text{Br}}^{ - } }}$$
(3)
$$C_{{{\text{Br}}_{2} }}^{\prime } = 0.5k_{1} \cdot C_{{{\text{Br}}^{ - } }}$$
(4)
$$k_{1} = \frac{{{\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}{{1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}.$$
(5)
Fig. 1

Schematic of the axisymmetric two-dimensional model used for the simulation of the whole bulk solution system, where a is the diameter of the Pt wire, b is the diameter of sealing glass outside the Pt wire, d is the distance between the tool electrode and the GaAs substrate, and h is the etching depth of GaAs substrates. The bold boundary represents the model boundary defined in the text, and the dotted line represents the cross section of the etching contour

3 Experimental Section

3.1 Chemical Solutions and Materials

The analytical grades of H2SO4 and NaBr were provided by Sinopharm Chemical Reagent Co., Ltd. (China). All of the experimental solutions were prepared with deionized water (18.2 MΩ, MilliQ, Millipore, Bedford, MA, USA). The substrates were silicon-doped GaAs wafers with a doping level between 0.8 × 1018 and 2.3 × 1018 cm−3 provided by Chinese Crystal Technologies Co., Ltd. (Hefei, China). Before being used, the substrates were cleaned with deionized water and dried with nitrogen.

3.2 Instrumentation and Procedures

All experiments were performed on the reported homemade precision machining system [22]. The experimental electrode included a tool electrode, a reference electrode, and a counter electrode. The counter electrode was a Pt wire, and the reference electrode was an Ag wire with the same size of 500-μm diameter. The tool electrode was a 100-μm-diameter Pt wire with the sealing glass that had been tapered to RG = 2. The tool electrode had been characterized by optical microscopy and a steady-state volt–ampere curve. The reported tool electrode worked in solution containing 100 mM NaBr and 500 mM H2SO4. All the simulations were calculated by COMSOL Multiphysics 5.3 software (Stockholm, Sweden).

4 Results and Discussion

4.1 Variations of the Tip Current in Steady-State Simulation

There are many factors affecting the etching results. We do not discuss the influence of solution concentration on the etching results here. We fixed the etching concentration as 100 mM NaBr and 500 mM H2SO4. The tip diameter in the following simulation process was set at 100 μm, and the influence of the tip diameter was not introduced in this study.

The voltage of the tool electrode and the distance between the tool electrode and the GaAs substrate are two important factors. To integrate both factors into the etching function, we studied the relationship between these two factors and the tip current. The parameter d (the distance between the tool electrode and the GaAs substrate) was selected between 5 and 25 μm. As illustrated in Fig. 2a, as parameter d increases, the tip current decreases, and all the curves coincide after being normalized. This means that the relationship between the voltage of the tool electrode and d is consistent. By mathematical fitting curves, we obtained the mathematical formula as expressed by function (6); the coefficient of association was greater than 0.99
$$I(\upmu{\text{A}}) = f(E) \times d(\upmu{\text{m}})^{ - \,0.583} .$$
(6)
Fig. 2

Steady-state simulation results of the etching process: a the relationship between the tip current and the distance between the tool electrode and the GaAs substrate at the tool electrode voltages of 0.90, 0.95, 1.00 and 1.10 V, and b the relationship between the tip current and the tool electrode voltage at distances between the tool electrode and the GaAs substrate of 5, 10, 15, 20 and 25 μm

To calculate the specific function of the voltage in the function, we analyzed the relationship between the voltage of the electrode and the tip current, as illustrated in Fig. 2b. All the curves also agree after normalization. According to the energy potential function mentioned above, the relationship between all curves would be consistent. After fitting all curves and substituting function (6), function (7), which is the function of the voltage, was obtained, and the coefficient of association was greater than 0.99
$$f(E) = 29.1881 - \frac{29.1881}{{1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}.$$
(7)
Thus, we obtained the relationship among three factors mentioned above as indicated in function (8)
$$I\left( {\upmu{\text{A}}} \right) = \left( {29.1881 - \frac{29.1881}{{1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}} \right) \times d\left( {\upmu{\text{m}}} \right)^{ - \,0.5825} .$$
(8)

This formula can be used to predict the tip current under the above-mentioned research situation before an experiment. Additionally, through coupling the voltage of the tool electrode and the distance between the tool electrode and the GaAs substrate, we could face the resolution limit problem caused by the single factor.

4.2 Calculation of the Etching Function in Transient Simulation

By introducing the parameter of machining time, the relationship between tip current and etching depth was studied, and the etching function was obtained during simulations on the relationship between these factors (tip current, voltage of the tool electrode, machining time and distance between the tool electrode and GaAs substrates) and etching depth under the transient condition.

The distance between the tool electrode and GaAs substrates was set at 10 μm, and the relationship between etching times and the etching depth was simulated at different times, as illustrated in Fig. 3a. With the increase of the etching time, the distance between the tool electrode and the GaAs substrate was divided into two parts: the parameter d and the parameter h.
Fig. 3

Transient simulation results of etching process: a the relationship between tip current and the tool electrode voltage at the machining times of 25, 50, 100, 150, and 200 s. b The change trend of current with time at different tool electrode voltages of 0.90, 0.95, 1.00, and 1.10 V. c The relationship between etching depth and the tool electrode voltage at machining times of 10, 100, 200, and 400 s. d The etching contour under tool electrode voltages of 0.85, 0.90, 0.95, 1.00, and 1.05 V. e The etching contour at machining times of 10, 100, 200, and 400 s. f The relationship between etching depth and tip current. g The etching contour rate at machining times of 10, 100, 200, and 400 s. h The relationship between etching depth and the tool electrode voltage or the tool electrode voltage rate

Thus, parameter h, which increases across time, would have a more obvious impact on the tip current. In addition, the relationship between the voltage of the tool electrode and the tip current would vary with time. Moreover, the function (8) was modified as indicated by function (9) and function (10), and the coefficient of association was greater than 0.99
$$I\left( t \right)\,\left( {\upmu{\text{A}}} \right) = \frac{{c_{1} (t) \times {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}{{1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }} \times d^{ - \,0.5825} \left( {\upmu{\text{m}}} \right)$$
(9)
$$c_{1} (t) = 0.0757 - 0.0268 \times \left( {1 - {\text{e}}^{{ - \frac{t}{1162.7792}}} } \right) - 0.0130 \times \left( {1 - {\text{e}}^{{ - \frac{t}{169.8562}}} } \right).$$
(10)

According to this function, the tip current changed with etching time, the voltage of the tool electrode, and the distance between the tool electrode and GaAs substrates. To further modify the etching function, we set the distance between the tool electrode and GaAs substrates at 100 μm to simulate the relationship between the tip current and the other two factors. The simulation results are illustrated in Fig. 3b. The tip current decreases with the increase of etching time, and the greater the voltage of the tool electrode, the more obvious the decrease. This is consistent with function (10), with the error less than 5% and the machining time within 100 s.

Based on the definition of the relationship between the tip current and the above parameters, the relationship between the etching depth and these factors was directly studied. The etching function depends on many parameters, such as the voltage of the tool electrode and the machining time. The simulation results between the maximum etching depth at different etching times and the voltage of the tool electrode are illustrated in Fig. 3c. Similarly, as the etching time increases, the etching depth increases at different voltages, and the relationship between the two is not linear. By fitting these curves, we found that the results were consistent with the above conclusion that the etching depth would increase the distance between the tool electrode and GaAs substrates and affect the final etching depth.

To further analyze the relationship between etching depth and these two factors, these factors were analyzed separately. We analyzed the etching depth under different volts of the tool electrode with a fixed etching time of 100 s. There are two aspects of the influence about the voltage of the tool electrode on etching results: etching profile and etching depth. First, we studied the effect of the voltage of the tool electrode on etching profile. The etching profile within the range of 0.85–1.20 V at 100 s can be obtained by simulation, as illustrated in Fig. 3d. As the voltage of the tool electrode increases, the etching depth increases. The normalized contours remain the same. The differences between contours under different voltages are only different in size. The overall shape is smooth, and there is no inflection point. Although the tool electrode is a 100-μm-diameter Pt disk, the side wall of the etched profile obtained by the simulation is not straight because of the diffusion effect, and there would be a slope. Additionally, the bigger the voltage, the bigger the slope. Similarly, the machining contours at different etching times under 1.2 V were analyzed, as illustrated in Fig. 3e. The maximum etching depth increases with etching time, and the contour slope increases.

By analysis of the relationship between tip current and the maximum etching results, we would directly obtain the relationship between the voltage of the tool electrode and etching depth. As illustrated in Fig. 3f, we simulated the relationship between tip current and the maximum etching depth under different volts of the tool electrode at the etching time of 100 s. Their relationship is obviously linear. After straight-line fitting, we found function (11), and the coefficient of association was greater than 0.99
$$h\left( {100} \right)\,\left( {\upmu{\text{m}}} \right) = - \,0.3403I\,\left( {\upmu{\text{A}}} \right).$$
(11)
If we combine function (9) and function (11), we get an etching function that describes the relationship between the voltage of the tool electrode and etching depth as indicated in function (12)
$$h(t)\,(\upmu{\text{m}}) = - \,0.3403 \times \frac{{c_{1} (t) \times {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}{{\mathop {1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }\limits^{{}} }} \times d^{ - \,0.5825} (\upmu{\text{m}}) .$$
(12)
To further analyze and evaluate the shape of contours, the trend of contours’ variation was analyzed, as illustrated in Fig. 3g. We differentiated the function of the etched contour and the voltage of the tool electrode. It can be seen that the change rate of the contours’ center and the part away from the tool electrode is almost zero. The overall trends increase with etching time, and the parts of contours under different etching times that start changing are all smaller than 50 μm, which is the radius of the tool electrode. The maximum change occurs at the outer edge of the tool electrode, which is the part larger than the diameter of the tool electrode. Moreover, the maximum change area is also extending to the outside of the tool electrode as etching time increases. This means that the effective region expands with etching time. The shape of etching contours would have an impact on the machining results. Therefore, the reasonable selection of etching time and the voltage of the tool electrode is crucial. The relatively special structures can be machined, and various factors of machining can be optimized to improve the machining efficiency. Additionally, we focused on the effect about the voltage of the tool electrode on the maximum etching depth. Simulation results between the maximum etching depth at the etching time of 100 s and the voltage of the tool electrode are illustrated by the black line in Fig. 3h. Simulation results between the rate of the maximum etching depth changing and the voltage of the tool electrode are illustrated by the blue line in Fig. 3h. Results are similar with the simulation results under different etching times. The fitting function of the maximum etching depth at 100 s and the voltage of the tool electrode is depicted by function (13), and the coefficient of association is greater than 0.99
$$h(100)\,(\upmu{\text{m}}) = - \,2.5709 \times \frac{{{\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}{{1 + {\text{e}}^{{\frac{E(V) - 0.9807}{0.02568}}} }}.$$
(13)

This function is consistent with function (10), and the maximum etching depth is approximately 2.57 μm.

4.3 Experimental Verification of the Etching Function

To further verify the etching function and give the function error, the static etching experiment was carried out. The tool electrode was placed 10 μm away from the GaAs substrate, and the voltage of the tool electrode was randomly selected in the range of 0.8–1.2 V. The machining time was set as 100 s, and the experimental results were recorded. The tip current decreased with time as machining time decreased, and it was consistent with that in Fig. 3b above. We set the tip current of machining time at approximately 100 s and compared the relationship between the voltage of the tool electrode and tip current under the simulation process and the experimental process. The results are basically the same as those illustrated in Fig. 4a. We selected the maximum etching depth at the interface of the etching contour center and compared the relationship between the voltage of the tool electrode and etching depth under the simulation process and the experimental process. Although the trends are consistent, the results vary slightly due to factors such as solution purity and the tool electrode wear, as illustrated in Fig. 4b. After Boltzmann curve fitting, we found function (14), and the coefficient of association was greater than 0.99
$$h(100)\,(\upmu{\text{m}}) = - \,2.5057 \times \frac{{{\text{e}}^{{\frac{E(V) - 0.9799}{0.02651}}} }}{{1 + {\text{e}}^{{\frac{E(V) - 0.9799}{0.02651}}} }}.$$
(14)
Fig. 4

Etching results of the etching experiments: a the relationship of tip current and the tool electrode voltage measured (solid line) and simulated by geometry model (symbols). b The etching contour measured (solid line) and simulated by geometry model (symbols). c The etched pits were measured by the 100-nm-diameter diamond tip under constant force of 2 mN at different tool electrode voltages of 1.20, 1.15, 1.13, 1.10, 1.07, 1.05, 1.03, 1.00, 0.99, 0.97, 0.95, 0.93, 0.90, and 0.85 V

When functions (13) and (14) were compared, the maximum error of parameters was less than 3%. As illustrated in Fig. 4c, the machining contour maps at different voltages of the tool electrode were consistent. The etching depth decreased as the voltage of the tool electrode decreased, and the overall etching contours were consistent with the simulation results. According to the reported results, the slight difference was caused by tip passivation [20].

5 Conclusions

We put forward the concept of an etching function to study the process of scanning electrochemical microscopy. First, we studied the relationship by simulation in the process of etching GaAs with NaBr. The relationship between many factors (including machining time, the distance between the tool electrode and GaAs substrates, and the voltage of the tool electrode and tip current) and etching depth was analyzed under steady-state and transient simulation processes, respectively. Then, we established the multifactor etching function and analyzed each single factor. The etching function of the voltage of the tool electrode, the etching depth at the machining time of 100 s, and the parameter d of 10 μm was determined. Moreover, this relationship was verified by experiments and is consistent with that of the simulation results. In conclusion, this model can be used to determine the etching functions under different factors to determine the etching depth by adjusting different parameters. This work also applies to research on the etching function of the other III semiconductors.

Notes

Acknowledgements

The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (21827802, 21573054, 21327002) and the Fundamental Research Funds for the Central Universities (20720190023).

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflicts of interest.

References

  1. 1.
    Bard AJ, Fan FRF, Kwak J, Lev O (1989) Scanning electrochemical microscopy. Introduction and principles. Anal Chem 61(2):132–138CrossRefGoogle Scholar
  2. 2.
    Ventosa E, Schuhmann W (2015) Scanning electrochemical microscopy of Li-ion batteries. Phys Chem Chem Phys 17(43):28441–28450CrossRefGoogle Scholar
  3. 3.
    Mirkin MV, Nogala W, Velmurugan J, Wang YX (2011) Scanning electrochemical microscopy in the 21st century. Update 1: five years after. Phys Chem Chem Phys 13(48):21196–21212CrossRefGoogle Scholar
  4. 4.
    Kai T, Zoski CG, Bard A (2018) Scanning electrochemical microscopy at the nanometer level. Chem Commun 54:1934–1947CrossRefGoogle Scholar
  5. 5.
    Polcari D, Dauphin-Ducharme P, Mauzeroll J (2016) Scanning electrochemical microscopy: a comprehensive review of experimental parameters from 1989 to 2015. Chem Rev 116(22):13234–13278CrossRefGoogle Scholar
  6. 6.
    Izquierdo J, Knittel P, Kranz C (2018) Scanning electrochemical microscopy: an analytical perspective. Anal Bioanal Chem 410(2):307–324CrossRefGoogle Scholar
  7. 7.
    Mandler D, Bard AJ (1990) Hole injection and etching studies of gallium arsenide using the scanning electrochemical microscope. Langmuir 6(9):1489–1494CrossRefGoogle Scholar
  8. 8.
    Mirkin MV, Horrocks BR (2000) Electroanalytical measurements using the scanning electrochemical microscope. Anal Chim Acta 406(2):119–146CrossRefGoogle Scholar
  9. 9.
    Zheng Q, Huang X, Liu Y, Fang X, Zhang J, Shao H (2017) Electrochemical quantification of intermolecular hydrogen bonding between ferrocenemethanol and 3-mercaptopropanoic acid on gold. J Phys Chem C 121(40):22123–22129CrossRefGoogle Scholar
  10. 10.
    Zheng Q, Zhang J, Yang Y, Wang X, Ding K, Shao H (2017) Regulating the intermolecular hydrogen bonding: the reversible assembly and disassembly in the diffusion layer. J Electrochem Soc 164(2):H97–H103CrossRefGoogle Scholar
  11. 11.
    Zhang J, Lai J, Wang W, Huang P, Jia JC, Han L et al (2017) Etching kinetics of III–V semiconductors coupled with surface passivation investigated by scanning electrochemical microscopy. J Phys Chem C 121(18):9944–9952CrossRefGoogle Scholar
  12. 12.
    Shuming Y, Linlin Y, Guofeng Z, Tong W, Xiaokai Y (2018) Modeling and calibration of the galvanometric laser scanning three-dimensional measurement system. Nanomanuf Metrol 1(3):180–192CrossRefGoogle Scholar
  13. 13.
    Michihata M, Kim J, Takahashi S, Takamasu K, Mizutani Y, Takaya Y (2018) Surface imaging technique by an optically trapped microsphere in air condition. Nanomanuf Metrol 1(1):32–38CrossRefGoogle Scholar
  14. 14.
    Shimizu Y, Matsuno Y, Chen YL, Matsukuma H, Gao W (2018) Design and testing of a micro-thermal sensor probe for nondestructive detection of defects on a flat surface. Nanomanuf Metrol 1(1):45–57CrossRefGoogle Scholar
  15. 15.
    Mirkin MV, Bard AJ (1992) Multidimensional integral equations: a new approach to solving microelectrode diffusion problems: part 2. Applications to microband electrodes and the scanning electrochemical microscope. J Electroanal Chem 323(1–2):29–51CrossRefGoogle Scholar
  16. 16.
    Bard AJ, Mirkin MV, Unwin PR, Wipf DO (1992) Scanning electrochemical microscopy. 12. Theory and experiment of the feedback mode with finite heterogeneous electron-transfer kinetics and arbitrary substrate size. J Phys Chem 96(4):1861–1868CrossRefGoogle Scholar
  17. 17.
    And YS, Mirkin MV, Fish G, Kokotov S, Daniel Palanker A, Lewis A (1997) Nanometer-sized electrochemical sensors. Anal Chem 69(8):1627–1634CrossRefGoogle Scholar
  18. 18.
    Klusmann E, Schultze JW (1997) Ph-microscopy—theoretical and experimental investigations. Electrochim Acta 42(20):3123–3134CrossRefGoogle Scholar
  19. 19.
    Zheng Q, Wei GW (2011) Poisson–Boltzmann–Nernst–Planck model. J Chem Phys 134(19):194101CrossRefGoogle Scholar
  20. 20.
    Lianhuan H, Xuesen Z, Zhenjiang H (2017) Tip current/positioning close-loop mode of scanning electrochemical microscopy for electrochemical micromachining. Electrochem Commun 82:117–120CrossRefGoogle Scholar
  21. 21.
    Zhang J, Jia J, Han L, Yuan Y, Tian ZQ, Tian ZW (2014) Kinetic investigation on the confined etching system of n-type gallium arsenide by scanning electrochemical microscopy. J Phys Chem C 118(32):18604–18611CrossRefGoogle Scholar
  22. 22.
    Yuan Y, Han L, Huang D (2015) Electrochemical micromachining under mechanical motion mode. Electrochim Acta 183:3–7CrossRefGoogle Scholar

Copyright information

© International Society for Nanomanufacturing and Tianjin University and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Micro-systems and Micro-structures Manufacturing of Ministry of EducationHarbin Institute of TechnologyHarbinPeople’s Republic of China
  2. 2.State Center for Precision EngineeringHarbin Institute of TechnologyHarbinPeople’s Republic of China
  3. 3.State Key Laboratory of Physical Chemistry of Solid Surfaces (PCOSS), Collaborative Innovation Center of Chemistry for Energy Materials (iChem), Engineering Research Center of Electrochemical Technologies of Ministry of Education, and Department of Chemistry, College of Chemistry and Chemical EngineeringXiamen UniversityXiamenPeople’s Republic of China
  4. 4.Department of Mechanical and Electrical Engineering, School of Aerospace EngineeringXiamen UniversityXiamenPeople’s Republic of China

Personalised recommendations