## 1.

## Introduction

For large-aperture, high-power laser systems, such as the National Ignition Facility in the United States,^{1}^{,}^{2} Laser Megajoule in France,^{3} and the SGIII laser facility in China,^{4} the ultraviolet optical lifetime of fused silica must be increased. The polishing contaminants in the near-surface region of optical components can absorb sub-band gap light and produce a local heating that can initiate a material damage.^{5} Many experimental facts have shown that absorbing nanometer-sized inclusions are responsible for the initiation of the damage process: an increase of the damage thresholds with purification of subsurface of fused silica;^{6}^{,}^{7} a spatial variation of the damage threshold on the surface or in bulk of optical substrates;^{8}^{,}^{9} and a dependence of the damage threshold on the irradiation spot-size and wavelength.^{10}^{,}^{11} However, in most cases, the impurities are not identified by modern optical techniques since they are nanoscale size and are distributed at low concentration.^{12}

It is obvious that the inclusion-initiated damage has a statistical character because of the spatial distribution of inclusions in a sample.^{10} The theoretical studies of inclusion-initiated damage were based on the resolution of Fourier equation. ^{13}14.15.^{–}^{16} However, these models have not been substantiated enough to explain the statistical character in experiments. The information on damage density and damage threshold of precursors can be extracted from the experimental curves of damage probability.^{11}^{,}^{17} Feit and Rubenchik have presented a model^{18} that the size distribution of nanoabsorbers is related to the damage density and damage probability, which predicts the dependence of damage density on pulse duration.

In this paper, we go further to relate the contents of various impurities measured from the subsurface layers of different samples to damage probability. In Sec. 2, based on calculation of absorption of spherical particles and then solving the heat equation, for various particles, the critical fluence required to initiate damage can be calculated. Considering the fit distribution parameters, the laser damage probability on the surface of fused silica has been calculated. In Sec. 3, the subsurface components of impurities for different samples are determined by inductively coupled plasma optical emission spectrometry (ICP-OES) and the data points of laser damage probability have been measured. Subsequently, the theoretical model presented has been used for analyzing the effect of various impurities on damage probability.

## 2.

## Theoretical Model

## 2.1.

### Critical Fluence

Contaminants detected include the major polishing compound components (Ce or Zr from ${\mathrm{CeO}}_{2}$ or ${\mathrm{ZrO}}_{2}$), and other metals (Fe, Cu, Cr) induced by the polishing step or earlier grinding steps. Al is present largely because of the use of ${\mathrm{Al}}_{2}{\mathrm{O}}_{3}$ in the final cleaning process. ${\mathrm{Al}}_{2}{\mathrm{O}}_{3}$ and ${\mathrm{ZrO}}_{2}$ are nonabsorbing materials at 355 nm, so we just consider ${\mathrm{CeO}}_{2}$, Cu, Fe, and Cr particles in the simulation. With the improvement of surface-micromachining process, few 100-nm particles can be identified by classical optical techniques and can be removed from the subsurface of fused silica, so the particle radius of $<100\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mathrm{nm}$ was simulated in the model. For simplification, we only consider the shape of a sphere, although it is not necessarily needed in all cases.^{19} The temperature distribution is necessary for evaluating the critical fluence required to initiate damage, and the spherical particle heating under the laser radiation is described by the equation of heat conduction.

## (1)

$${C}_{i}(T){\rho}_{i}\frac{\partial {T}_{i}}{\partial t}=\nabla [{\chi}_{i}(T)\nabla {T}_{i}]+\frac{\sigma I}{V}f(t)\theta (R-r),$$^{22}

## (3)

$${Q}_{\text{sca}}=\frac{2\pi}{{k}^{2}}\sum _{n=1}^{\infty}(2n+1)({|{a}_{n}|}^{2}+{|{b}_{n}|}^{2}),$$We plotted in Fig. 1 the absorption cross-section of various particles (${\mathrm{CeO}}_{2}$, Cu, Fe, and Cr) embedded in fused silica. We can see from Fig. 2 that the absorptivity of ${\mathrm{CeO}}_{2}$ particles is much lower than others (Cu, Fe, and Cr) with the same size.

Considering the Fourier transform of the temperature, Eq. (1) can be written as

## (5)

$$\frac{1}{{r}^{2}}\frac{\partial}{\partial r}\left({r}^{2}\frac{\partial {\widehat{T}}_{i}}{\partial t}\right)+{\alpha}_{i}^{2}{\widehat{T}}_{i}=-\frac{\sigma I\theta (R-r)}{2V{\chi}_{p}(T)}\sqrt{\pi}\tau \mathrm{exp}(-\frac{{\tau}^{2}{\omega}^{2}}{16}),$$## (6)

$${\widehat{T}}_{p}(r,\omega )=\frac{{A}_{p}I}{r}[\mathrm{exp}(i{\alpha}_{p}r)-\mathrm{exp}(-i{\alpha}_{p}r)]\phantom{\rule{0ex}{0ex}}-\frac{\sigma I}{2V{\alpha}_{p}^{2}{\chi}_{p}(T)}\sqrt{\pi}\tau \mathrm{exp}(-\frac{{\tau}^{2}{\omega}^{2}}{16}),$$^{23}Thus, the critical fluence ${F}_{c}$ required to reach the critical temperature can be expressed as

## (8)

$${F}_{c}={\int}_{-\infty}^{+\infty}If(t)\mathrm{d}t\phantom{\rule{0ex}{0ex}}={(\pi )}^{\frac{3}{2}}R\tau {T}_{c}{[\underset{t}{\mathrm{max}}(\sum _{\omega =-N}^{N}{A}_{h}\mathrm{exp}(i{\alpha}_{h}R-i\omega t)\mathrm{\Delta}\omega )]}^{-1}\mathrm{.}$$We consider that various particles embedded in fused silica are irradiated at 355 nm during pulse duration of 10 ns, and the critical fluence as a function of particle radius has been plotted in Fig. 2.

We can see from Fig. 2 that ${\mathrm{CeO}}_{2}$ particles require higher fluence to initiate damage when the particle radius is $<100\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mathrm{nm}$.

## 2.2.

### Laser Damage Probability on the Surface of Optical Materials

We assume that the breakdown is reached if a particle is irradiated with fluence higher than ${F}_{c}$, and the damage probability can be theoretically calculated based on the distribution law of particles. When the damage precursors are assumed to be subsurface inclusions, the laser damage probability can be expressed as a function of fluence $F$.^{10}

^{19}

## (10)

$$n(R)=\frac{(\gamma -1){d}_{0}}{{R}_{\mathrm{min}}^{1-\gamma}-{R}_{\mathrm{max}}^{1-\gamma}}{R}^{-\gamma},$$^{24}), and ${d}_{0}$ is the density of particles per unit of surface. Based on the relationship between critical fluence and particle size, the upper limit ${R}_{\mathrm{max}}$ can be obtained from measured damage threshold and the lower limit ${R}_{\mathrm{min}}$ can be obtained where the experimental damage probability is 1. The relationship between $g({F}_{c})$ and density of particles ${d}_{0}$ is

With this model we have the ability to describe laser damage on the surface of fused silica as function of fluence $F$ by choosing two physical characteristics: the size distribution of particles $\gamma $ and their density ${d}_{0}$ on the subsurface of optical materials. By choosing the fit distribution parameters ${d}_{0}$ and $\gamma $, we can insert the ${R}_{\mathrm{min}}$ and ${R}_{\mathrm{max}}$ from sample S1 (see Table 1) to calculate the laser-induced damage probability based on the relationship between critical fluence and particle radius.

## Table 1

The values for Rmin and Rmax of different particles from samples S1 to S4.

Rmin, Rmax (nm) | CeO2 | Cu | Fe | Cr |
---|---|---|---|---|

S1 | 37, 50 | 9, 13 | 11, 15 | 13, 16 |

S2 | 33, 45 | 8, 11 | 10, 13 | 12, 14 |

S3 | 32, 38 | 7, 10 | 9, 12 | 11, 13 |

S4 | 30, 36 | 6, 9 | 8, 11 | 10, 12 |

Figure 3 shows that damage probability initiated by ${\mathrm{CeO}}_{2}$ particles increases as the density of particles ${d}_{0}$ increases, and decreases as the parameter of size distribution $\gamma $ increases. In order to identify the influence of various particles on damage probability, we plotted in Fig. 4 the curves of laser damage probability initiated by various particles calculated with same parameters ${d}_{0}=1\times {10}^{6}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{\mathrm{mm}}^{2}$ and $\gamma =3$. From Fig. 4, we can see that considering the size distribution from sample S1 as seen in Table 1, ${\mathrm{CeO}}_{2}$ particles have a greater damage probability than others (Cu, Fe, and Cr) with the same distribution parameters ${d}_{0}$ and $\gamma $.

## 3.

## Experiment

The experimental setup for laser-damage test has been described in detail elsewhere,^{11}^{,}^{17} and only a brief description is given here. The data points of laser damage probability are measured at 355 nm using injected Nd:YAG laser with the Gaussian temporal profile. The effective pulse duration (at $1/\mathrm{e}$) is 10 ns. In order to obtain typical damage probability in larger range of fluence, the small spot diameter of 8 *μ*m (at $1/{e}^{2}$) is chosen in the test. The error of measured spot diameter is $\sim 140\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mathrm{nm}$. The damage test 1-on-1 is applied with a large number of points to obtain a reliable measurement. We observe the 50 different regions under the laser irradiation at each fluence $F$, and each data point $P(F)$ is plotted by counting the number of damage regions at each fluence $F$. Energy of the incident beam is measured with a calorimeter, and the fluence fluctuations have a standard deviation of $\sim 10\%$. To have a good accuracy of measurement, the test procedure of damage probability is repeated 10 times and the deviation $\u25b5P$ of average value is $<0.08$. In order to identify the effect of the contents of various impurities on laser damage probability, the components of impurities from subsurface layer are determined by ICP-OES and the data points of damage probability have been measured.

The fused silica samples (S1, S2, S3, S4) polished by cerium oxide slurry with different polishing levels were used in the experiment. Because of insufficient polishing process, there are more structural defects (per area), such as submicroscopic cracks, pores, and indentations, observed on the surface of samples S3 and S4. The size of the samples is $35\times 35\times 3\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mathrm{mm}$. After accurate weighing and thickness measurement, $\sim 1\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mu \text{m}$ of fused silica was digested by ultrapure grade hydrofluoric acid solution during 7 min. The masses of subsurface layer digested, respectively, were 0.00215, 0.00243, 0.00256, and 0.002695 g. The contents of impurities can be obtained by suitable spectral analysis. The contents of ${\mathrm{CeO}}_{2}$ and ${\mathrm{Al}}_{2}{\mathrm{O}}_{3}$ incorporated during polishing and cleaning process can be calculated based on the contents of Ce and Al measured by ICP-OES. Table 2 gives the contents of main impurities from the subsurface layer.

## Table 2

The contents of main impurities from subsurface layer of fused silica (μg/g).

Sample | CeO2 | Cu | Fe | Al2O3 | Cr |
---|---|---|---|---|---|

S1 | 1484.2 | 17.6 | 37.1 | 19.4 | 16.4 |

S2 | 937.3 | 15.2 | 27.3 | 17.3 | 8.6 |

S3 | 638.9 | 12.8 | 39.2 | 22.6 | 13.2 |

S4 | 333.8 | 6.68 | 23.5 | 15.4 | 5.6 |

We can see from Table 2 that the contents of ${\mathrm{CeO}}_{2}$ impurities have much more than others and have large distinction in different samples. In order to relate the contents of various impurities to damage probability, the impurities are assumed to be spherical and their mass $m$ (per area) has a homogeneous distribution on the subsurface of fused silica. Thereby, the density (per area) of particles ${d}_{0}$ can be calculated from Eq. (10).

## (12)

$${d}_{0}=\frac{m}{\rho S}{\left[{\int}_{{R}_{\mathrm{min}}}^{{R}_{\mathrm{max}}}\frac{4}{3}\pi \frac{(\gamma -1)}{{R}_{\mathrm{min}}^{1-\gamma}-{R}_{\mathrm{max}}^{1-\gamma}}{R}^{3-\gamma}\mathrm{d}R\right]}^{-1},$$^{25}

Substituting Eq. (13) into Eq. (9), the curves of damage probability from samples S1 to S4 can be calculated. The scheme for calculation has been presented in Fig. 5.

Figure 6 shows the experimental data points of damage probability measured on the surface of fused silica and theoretical curves initiated by impurities. As seen in Fig. 6, the smaller particle is required to absorb more fluence to reach breakdown. Thus, the damage threshold increases from samples S1 to S4 because the upper limit ${R}_{\mathrm{max}}$ decreases as seen in Table 1. Cu and Cr impurities have a very weak influence on experimental damage probability since their contents on the subsurface of the samples are very low. On the contrary, ${\mathrm{CeO}}_{2}$ and Fe impurities are closely related to the damage probability when the levels of contents are high as seen in sample S1. We can also notice that for the samples with low ${\mathrm{CeO}}_{2}$ contents (S2, S3, and S4), this correlation is weaker, and it has a good agreement with experimental data on ${\mathrm{CeO}}_{2}$ contents dependence of damage density.^{26} In the case of ${\mathrm{CeO}}_{2}$ impurities, as the dramatic decrease of the contents from samples S1 to S4, the damage density will decrease according to our calculation. As a consequence, the damage probability induced by the laser pulse with same fluence will decrease. Obviously, a large discrepancy is found between theory and experiment in samples S3 and S4 since there are more structural defects located on the subsurface of samples from insufficient polishing process. These structural defects with a spatial distribution add the absorbing centers near the surface^{27} and cause more damage sites than expected from the distribution of impurities, so the measured laser damage probability is found to be larger than theoretical calculation.

## 4.

## Conclusion

A model has been presented in order to relate the distribution properties of various impurities on the subsurface of fused silica to damage probability. The theoretical curves of damage probability initiated by the impurities having a given density and size distribution have been obtained. The data points of damage probability on the surface of fused silica have been measured. Meanwhile, the contents of impurities from the subsurface layer of fused silica have been determined by ICP-OES. The correlation of different contents of impurities to damage probability has been analyzed, and it has a good agreement with obtained results. This model is of interest for identifying the influence of various impurities induced by polishing, grinding, and cleaning processes on laser damage probability, and it can also be applied to investigate laser damage on surface of other optical substrates or films.

## Acknowledgments

This work was supported by Major Program of National Natural Science Foundation of China (60890200) and National Natural Science Foundation of China (10976017).

## References

## Biography

**Xiang Gao** received his BS degree from the Department of Applied Physics at Sichuan University of Science & Engineering in July 2009. Then, he was admitted to Sichuan University to pursue a PhD in optical engineering in 2011. His research work mainly focus on nanosecond/femtosecond laser–matter interactions at high intensities. Now he has published 11 papers in journals.

**Guoying Feng** graduated from Zhejiang University, Department of Optical Engineering, in 1969. She received a PhD degree in laser technology from Zhejiang University of College of Electronic Information in 1998. Her research is focused on laser physics and technology, laser beam propagation and control, laser-induced damage, etc. She published over 100 scientific papers in journals, conference proceedings, and books. She is also a professor in the Department of Optical Engineering of Sichuan University.

**Lingling Zhai** received her bachelor’s degree in 2007. She is currently enrolled in the College of Electronics and Information Engineering at Sichuan University and will receive a master’s degree in engineering in 2014. Her research area is laser-induced damage in optical components, particularly, fused silica. In 2013, she published a paper about laser damage mechanism induced by inclusions in fused silica in high-power laser and particle beams.

**Zhou Shouhuan** suggested the technical implementation idea of DPSSL and became one of the earliest researchers of DPSSL in China in the beginning of 1970s. He has won the Second Grade National Invention Prize and the Second Grade of the National Prize for Progress in Science and Technology. He was elected member of the Chinese Academy of Engineering in 2003.