An extensive theoretical quantification of secondary electron emission from silicon

Khan, Muhammad Saadat Shakoor; Mao, Shifeng; Zou, Yu-Gang; Lu, Dongsi; Da, Bo; Li, Y.G.; Ding, Zhe

doi:10.1016/j.vacuum.2023.112257

Cited by 8 publications

(4 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ϑ is the scattering angle, σ e is total elastic scattering cross-section and Ω is the solid angle; the direct scattering amplitude f (ϑ) and the spin reversal scattering amplitude g (ϑ) are obtained by solving the Dirac's equation with a partial wave expansion method. The scattering potential used in the present calculation follows previous works [56][57][58] and contains two parts: the electrostatic interaction and the Furness-McCarthy exchange potential [59]. The Fermi distribution and the Dirac-Fock electron density [60] were used to describe the charge distribution of the nucleus and the electron cloud, respectively.…”

Section: Monte Carlo Modelmentioning

confidence: 99%

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM

Guo,

Miao,

Zou

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

The model-based library (MBL) method has already been established for the accurate measurement of critical dimension (CD) of semiconductor linewidth from a critical dimension scanning electron microscope (CD-SEM) image. In this work the MBL method has been further investigated by combing the CD-SEM image simulation with a neural network algorithm. The secondary electron linescan profiles were calculated at first by a Monte Carlo simulation method, enabling to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g., top CD, sidewall angle and height) for Si and Au trapezoidal line structures. The machine learning methods have then been applied to predicate the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviation of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. This result shows that the machine learning methods can be practically applied to the MBL method for the purpose of reducing the library size, accelerating the construction of the MBL database and enriching the content of an available MBL database.

show abstract

Section: Monte Carlo Modelmentioning

confidence: 99%

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM

Guo,

Miao,

Zou

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This FPA has been demonstrated quite useful for the Monte Carlo simulation of secondary electron emissions from conductive materials [72], and it has been also employed in our previous calculations of SEM linescan profiles [43,46]. More recently, we have adopted the Levine-Louie (LL) model dielectric function [70] for the semiconductor material Si with a bandgap of 1.1 eV [59]. In this work, we also employed the LL model to the insulator material SiO 2 whose bandgap is much greater than that of Si.…”

Section: Monte Carlo Modelmentioning

confidence: 99%

“…Dapor et al conducted a detailed study on the simulation methods for investigating the interactions between electrons and the semiconductor material Si [57]. Ciappa et al [58] and Khan et al [59] calculated the secondary electron yield of the semiconductor element Si, whereas Hussain et al calculated the electron yields for compound semiconductor materials, such as, GaAs, InAs, and PbS [60]. Studies on the interactions between electrons and insulating materials have been reported, such as investigations on elemental diamond [61], compound SiO 2 [62,63], and polymethyl methacrylate [64].…”

Section: Introductionmentioning

confidence: 99%

Linewidth characterization of a self-traceable grating by SEM

Guo,

Miao,

Mao

et al. 2024

J. Phys. D: Appl. Phys.

Self Cite

View full text Add to dashboard Cite

In order to achieve high-precision nanometrology, a self-traceable grating reference material has been reported and prepared by atom lithography and soft X-ray interference techniques (J. Liu et al., Nanotechnology 32 (2021) 175301). In this work we employ a Monte Carlo simulation method to study the scanning electron microscopy (SEM) image contrast and linewidth characterization of the grating linewidth. The 3D structure of the mushroom-shaped grating line made of the multilayer (Pt, SiO2 and Si) is modeled according to the transmission electron microscopy (TEM) image, enabling the SEM linescan profiles of secondary electron signals to be obtained for different values of structural linewidth parameter from Monte Carlo simulations. In the light of the principle of the model-based library method a model database of Monte Carlo simulated SEM linescan profiles by varying the incident electron beam conditions and the grating linewidths is thus constructed; then the grating linewidth has been successfully characterized with experimental SEM image. By comparison with the TEM measurement, the measurement accuracy is verified to within 0.3% for the linewidth of ~25 nm.

show abstract

“…[1][2][3][4][5] A high secondary-electron yield (SEY) is required for scanning electron microscopy, particle accelerators and micro-channel plate detectors. [6][7][8][9][10] However, it results in performance degradation or even permanent damage in satellite microwave components and accelerators. [11][12][13][14] Thus, research on suppression of the SEY has received considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Physical mechanism of secondary-electron emission in Si wafers

Zhao 赵,

Meng 孟,

Peng 彭

et al. 2024

Chinese Phys. B

View full text Add to dashboard Cite

CMOS compatible RF/microwave devices such as filters and amplifiers, have been widely used in wireless communication systems. However, it often exists on secondary electron emission phenomenon among those RF/microwave devices based o silicon (Si) wafers, especially in high-frequency range. In this paper, we have studied the major factors that influence the secondary electron yield (SEY) in commercial Si wafers with different doping concentrations. It shows that SEY is suppressed as the increase of doping concentrations, corresponding to a relatively short effective escape depth λ. Meanwhile, the reduced narrow band gap is beneficial to suppress the SEY by through the first-principles calculations, in which the absence of shallow energy band below the conduction band would easily capture electrons. Thus, the new physical mechanism combined the effective escape depth and band gap, would provide useful guidance for the design of integrated RF/microwave devices based on Si wafers.

show abstract

An extensive theoretical quantification of secondary electron emission from silicon

Cited by 8 publications

References 102 publications

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM

Linewidth characterization of a self-traceable grating by SEM

Physical mechanism of secondary-electron emission in Si wafers

Contact Info

Product

Resources

About