1 Introduction
The 2023 Nobel Prize in Physics awarded to Pierre Agostini, Ferenc Krausz and Anne L’Huillier ‘for experimental methods that generate attosecond pulses of light for the study of electron dynamics in matter’[ 1 ] and the 2018 National Academies of Science report[ 2 ] that spurred the ‘Brightest Light Initiative’[ Reference Falcone, Albert, Beg, Glenzer, Ditmire, Spinka and Zuegel 3 ] highlight the impactful science enabled by producing the shortest possible pulses of light[ Reference Ferray, L’Huillier, Li, Lompre, Mainfray and Manus 4 – Reference Hentschel, Kienberger, Spielmann, Reider, Milosevic, Brabec, Corkum, Heinzmann, Drescher and Krausz 6 ]. Recent advances in high-energy few-cycle pulse generation techniques[ Reference Brabec and Krausz 7 , Reference Nagy, Simon and Veisz 8 ] allow us to probe new physical effects, including those from the carrier-envelope phase[ Reference Wittmann, Horvath, Helml, Schätzel, Gu, Cavalieri, Paulus and Kienberger 9 – Reference Huijts, Andriyash, Rovige, Vernier and Faure 11 ], and to generate high laser intensities with moderate laser energies for applications such as relativistic plasma-based attosecond pulse generation[ Reference Cavagna, Eder, Chowdhury, Kalouguine, Kaur, Mourou, Haessler and Lopez-Martens 12 ]. These developments motivate the design of optical components with higher laser-induced damage thresholds (LIDTs) for few- to single-cycle pulses. The scaling of LIDTs with laser fluence is generally well understood for the tens of picosecond to nanosecond regimes[ Reference Stuart, Feit, Herman, Rubenchik, Shore and Perry 13 ], but is more complex for shorter pulses[ Reference Du, Liu, Korn, Squier and Mourou 14 ], especially few-cycle pulses[ Reference Lenzner, Krüger, Sartania, Cheng, Spielmann, Kautek and Krausz 15 , Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer 16 ].
Previous experimental work has explored the damage and ablation of
${\mathrm{SiO}}_2$
using few-cycle approximately 800 nm wavelength pulses with durations of 5 fs[
Reference Lenzner, Krüger, Sartania, Cheng, Spielmann, Kautek and Krausz
15
,
Reference Lenzner, Krüger, Kautek and Krausz
17
,
Reference Kafka, Talisa, Tempea, Austin, Neacsu and Chowdhury
18
] and 7 fs[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
,
Reference Sanner, Utéza, Chimier, Sentis, Lassonde, Légaré and Kieffer
19
–
Reference Utéza, Sanner, Chimier, Brocas, Varkentina, Sentis, Lassonde, Légaré and Kieffer
21
]. Recently Kafka et al.
[
Reference Kafka, Talisa, Tempea, Austin, Neacsu and Chowdhury
18
] determined the damage threshold for different optical components with a 5 fs pulse and Talisa et al.
[
Reference Talisa, Alshafey, Tripepi, Krebs, Davenport, Randel, Menoni and Chowdhury
22
] explored the damage and ablation of
${\mathrm{SiO}}_2$
/
${\mathrm{HfO}}_2$
multi-layer coatings with 8 fs pulses. As highlighted in the review by Nagy et al.
[
Reference Nagy, Simon and Veisz
8
], post-compression techniques allow researchers around the world (e.g., Refs. [Reference Cavagna, Eder, Chowdhury, Kalouguine, Kaur, Mourou, Haessler and Lopez-Martens12,Reference Kowalczyk, Nagl, Steinleitner, Karpowicz, Pervak, Głuszek, Hudzikowski, Krausz, Mak and Weigel23–Reference Argenti, Chini and Fang25]) to generate few-cycle pulses, but traditional optics are easily damaged by these compressed pulses.
Understanding few-cycle pulse material interactions requires advances in computational and theoretical models. There has been significant work using one-dimensional finite-difference-time-domain (FDTD) simulations to model these interactions[ Reference Peñano, Sprangle, Hafizi, Manheimer and Zigler 26 ], with recent efforts in two dimensions[ Reference Zhang, Davenport, Talisa, Smith, Menoni, Gruzdev and Chowdhury 27 , Reference Zhang, Menoni, Gruzdev and Chowdhury 28 ]. FDTD simulations provide insight into the laser material interactions and can predict LIDTs, but do not capture the nonthermal nature of excited electrons, nor do they capture their ballistic motion. To expand our understanding of the interaction dynamics, we use particle-in-cell (PIC) simulations[ Reference Birdsall and Langdon 29 , Reference Hockney and Eastwood 30 ]. As with FDTD simulations, PIC codes solve Maxwell’s equations on a computational grid. In addition, PIC simulations statistically represent the neutral and excited particles in the simulation with a finite number of ‘macroparticles’[ Reference Birdsall and Langdon 29 – Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers 31 ]. The charged particles are then advanced using the Lorentz force and additional physical effects, including ionization and collisions, are often added.
PIC simulations are commonly used to study laser–plasma interactions (e.g., by Ziegler et al.
[
Reference Ziegler, Göthel, Assenbaum, Bernert, Brack, Cowan, Dover, Gaus, Kluge, Kraft, Kroll, Metzkes-Ng, Nishiuchi, Prencipe, Püschel, Rehwald, Reimold, Schlenvoigt, Umlandt, Vescovi, Schramm and Zeil
32
]) and are increasingly being modified to simulate laser damage and related regimes. For example, Mitchell et al.
[
Reference Mitchell, Schumacher and Chowdhury
33
] modeled crater formation in metals due to femtosecond laser ablation, Cochran et al.
[
Reference Cochran, Poole and Schumacher
34
] modeled liquid-crystal plasma mirrors[
Reference Corkum
35
], Déziel et al.
[
Reference Déziel, Dubé, Messaddeq, Messaddeq and Varin
36
] studied laser-induced periodic surface structures and Ding et al.
[
Reference Ding, Lim, Do, Xiong, Mahfoud, Png, Bosman, Ang and Wu
37
] and Do et al.
[
Reference Do, Jun, Mahfoud, Lin and Bosman
38
] modeled plasmons using PIC simulations. Interactions of lasers with nano/micro-scale structures in silicon and
${\mathrm{SiO}}_2$
have been modeled with versions of the framework introduced in this work[
Reference Shcherbakov, Sartorello, Zhang, Bocanegra, Bosch, Tripepi, Talisa, AlShafey, Smith, Londo, Légaré, Chowdhury and Shvets
39
,
Reference Smith, Zhang, Gruzdev and Chowdhury
40
] and recently Charpin et al.
[
Reference Charpin, Ardaneh, Morel, Giust and Courvoisier
41
] developed a similar framework to explore ionization in dielectrics. Our work builds on these efforts specifically for laser damage for few-cycle pulses by including the corrected Keldysh ionization model[
Reference Zhang, Menoni, Gruzdev and Chowdhury
28
,
Reference Keldysh
42
,
Reference Gruzdev
43
] to account for ionization across a range of fluences. We are able to explore the nonthermal distribution of excited particles in the laser damage regime using PIC simulation, which improves our fundamental understanding of damage mechanisms.
In this work, we begin in Section 2 by introducing the modifications and extensions we have made to a PIC code to model laser–dielectric interactions. Then in Section 3 we discuss the material properties and simulation setup for both bulk and multi-layer targets. Next, in Section 4 we compare the predictions of this framework to existing experimental results and discuss expected damage threshold metrics. Then we apply the framework to the modeling of multi-layer mirrors in Section 5 and conclude in Section 6.
2 Particle-in-cell simulation modifications
Our work extends the three-dimensional (3D) implementation of version 4.17.10 of the EPOCH[ Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers 31 ] PIC code, which is designed for the study of high-energy-density physics. EPOCH is a popular open-source PIC code that can scale to run on thousands of central processing unit (CPU) cores, although we note that there are a variety of other open-source and proprietary PIC codes available with different features and implementations[ Reference Smith, Orban, Rahman, McHugh, Oropeza and Chowdhury 44 ], such as graphics processing unit (GPU) operation (e.g., PIConGPU[ Reference Burau, Widera, Hönig, Juckeland, Debus, Kluge, Schramm, Cowan, Sauerbrey and Bussmann 45 ] and WarpX[ Reference Fedeli, Huebl, Boillod-Cerneux, Clark, Gott, Hillairet, Jaure, Leblanc, Lehe, Myers, Piechurski, Sato, Zaim, Zhang, Vay and Vincenti 46 ]). PIC simulations of laser–matter interactions are typically used to study only tens-to-hundreds of femtosecond timescales due to numerical instability issues and computational cost. As such, we focus on the initial laser–matter interaction and the resulting excited electron dynamics. For long-term dynamics and equilibration, one could use a final simulation state from a PIC model as the initial conditions for another model. For example, one could use the electron and ion temperatures in a two-temperature model, or consider tabulated equation of state values[ Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer 16 ].
EPOCH includes multiple physics modules, but was not designed with laser–dielectric interactions in mind. Towards this goal, we have added a new photoionization model relevant for dielectric materials and a model for optical material properties, as discussed in the next sections.
2.1 Keldysh photoionization
Our work extends the existing ionization framework already available in EPOCH[ Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers 31 ] to include the photoionization model developed by Keldysh[ Reference Keldysh 42 ]. The Keldysh ionization model allows us to calculate probabilities for electron transitions from the valence to the conduction band in solids due to the electric field of a laser pulse[ Reference Gruzdev 47 ].
For a laser pulse with electric field amplitude
$E$
and frequency
$\omega$
interacting with a material having band gap
$\Delta$
and reduced electron-hole mass
${m}^{\ast }$
, the Keldysh parameter is
$\gamma =\omega \sqrt{m^{\ast}\Delta}/ eE$
, where
$\gamma >>1$
is for the multiphoton ionization regime and
$\gamma <<1$
is for the tunneling regime[
Reference Keldysh
42
]. The Keldysh formulation is especially useful as it spans both regimes, which are often present when considering a laser-induced damage experiment. For example, the peak electric fields in our simulations give values of
$\gamma$
ranging from about 0.3 to 0.9, which means the photoionization regime is neither multiphoton nor tunneling, suggesting that the full Keldysh formula should be utilized.
The Keldysh model is used in our simulations to calculate the probability that a given neutral macroparticle will ionize. When ionization occurs, a new electron macroparticle and corresponding ion macroparticle are created in place of the original macroparticle. Now we introduce the ionization rate equation used in our work. For brevity in the following expressions, we follow Refs. [Reference Tien, Backus, Kapteyn, Murnane and Mourou48,Reference Balling, Schou and Rep49] by introducing the variables
${\gamma}_1={\gamma}^2/\left(1+{\gamma}^2\right)$
and
${\gamma}_2=1/\left(1+{\gamma}^2\right)$
. The effective band gap is then given by the following:

We may then write the ionization rate
$W$
as follows:

where
$\kappa$
and
$\epsilon$
are complete elliptic integrals of the first and second kind,
$\Phi$
is the Dawson integral and

We note that this is the corrected version of the formulation, where the original contains a misprint as noted by Gruzdev[ Reference Gruzdev 43 ]. Using the uncorrected version can result in significantly different calculations[ Reference Zhang, Menoni, Gruzdev and Chowdhury 28 , Reference Gruzdev 43 ].
For our simulation framework, the Keldysh ionization rate is evaluated in situ (with the exception of the elliptic integrals, which are read from tabulated data files with 1000 points) rather than interpolated from a tabulated form, which facilitates accuracy over a wide range of fluences. We use the first 500 terms in this infinite sum in Equation (3), which is sufficient for the regimes of interest in this paper, as illustrated in the appendix, while limiting the computational cost. An adaptive approach to a prescribed tolerance could be employed in the future. Currently each macroparticle can only be ionized once.
While the Keldysh parameter depends on the laser amplitude
$E$
, the amplitudes of our pulses vary within the laser envelope. Moreover, each computational cell in a PIC simulation only considers the instantaneous electric field. To address this, we store the electric field magnitude at each time step using an array large enough to store an entire laser cycle on a rolling window, as suggested by Zhang et al.
[
Reference Zhang, Menoni, Gruzdev and Chowdhury
28
]. Then the maximum field for the previous cycle is used to approximate the amplitude to calculate the ionization rate. This approach would encounter challenges for single-cycle pulses. In the future a more complex envelope model could be explored[
Reference Terzani and Londrillo
50
].
2.2 Collisional effects
We use the Pérez/Nanbu[
Reference Pérez, Gremillet, Decoster, Drouin and Lefebvre
51
,
Reference Nanbu and Yonemura
52
] binary collision module already included in the EPOCH code to account for collisions. The collision frequency in EPOCH is calculated for a charged particle
$\alpha$
with charge
${q}_{\alpha }$
scattering off a charged particle
$\beta$
as follows:

where
${n}_{\beta }$
is the density (for particles of species
$\beta$
),
$\ln \left(\Lambda \right)$
is the Coulomb logarithm,
$\mu ={m}_{\alpha }{m}_{\beta }/({m}_{\alpha }+{m}_{\beta })$
is the reduced mass and
${v}_\mathrm{r}$
is a relative velocity[
Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers
31
]. EPOCH extends the model to low temperatures following an approach by Pérez et al.
[
Reference Pérez, Gremillet, Decoster, Drouin and Lefebvre
51
] and Lee and More[
Reference Lee and More
53
]. More details can be found in the work of Arber et al.
[
Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers
31
] and in the source files and documentation provided with the open-source EPOCH code (https://epochpic.github.io/). Collisions are included between all charged particles in the simulation. The Coulomb logarithm is calculated automatically with a fixed lower bound of 1.
EPOCH includes a collisional ionization routine designed for atoms[
Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers
31
,
Reference Morris, Goffrey, Bennett and Arber
54
], but this is not well suited for impact ionization in dielectrics. A more appropriate ionization rate for our regime can be calculated with the approach by Keldysh[
Reference Keldysh
55
], although some of the input parameters to the models are not well reported and may require fitting of output results to experimental data[
Reference Charpin, Ardaneh, Morel, Giust and Courvoisier
41
,
Reference Déziel, Dubé and Varin
56
]. Impact ionization is reduced for shorter few-cycle pulses. Models by Petrov and Davis[
Reference Petrov and Davis
57
] suggest collisional ionization dominates photoionization for fluences exceeding
$0.4\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, whereas the multiple-rate-equation (MRE) model by Rethfeld[
Reference Rethfeld
58
] predicts this threshold to be
$10\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
[
Reference Balling, Schou and Rep
49
]. For this work, we do not include impact ionization, since we model few-cycle pulses that are only 7 fs (fewer than three laser cycles) full width at half maximum (FWHM) in duration. We find good agreement with previous experiments without the need for impact ionization, but expect this to be an important consideration for longer pulses in future work.
2.3 Refraction
The optical properties of dielectrics are modeled using a spatially varying permittivity
$\varepsilon$
throughout the simulation box. At the beginning of the simulation the optical properties are stored in a matrix with the same size as the simulation grid. We then modified the field solver to include a spatially dependent permittivity when advancing Maxwell’s equations, following a similar approach to that in the WarpX code[
Reference Fedeli, Huebl, Boillod-Cerneux, Clark, Gott, Hillairet, Jaure, Leblanc, Lehe, Myers, Piechurski, Sato, Zaim, Zhang, Vay and Vincenti
46
] and the modification to EPOCH by Charpin et al.
[
Reference Charpin, Ardaneh, Morel, Giust and Courvoisier
41
]. To easily account for arbitrary target structures, we define the shape of the optical region at the same place where particle species are initialized. There has been work including nonlinear optical effects in PIC and FDTD simulations[
Reference Déziel, Dubé, Messaddeq, Messaddeq and Varin
36
,
Reference Varin, Emms, Bart, Fennel and Brabec
59
], although those effects are not considered here.
3 Simulation setup
We begin by applying this simulation framework to a slab of fused silica (
${\mathrm{SiO}}_2$
) corresponding to the experiment discussed by Chimier et al.
[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
] and then apply the framework to a multi-layer mirror, as shown in Figure 1. We test a range of fluences near the reported damage threshold, where the fluence is given by
$F=2{E}_\mathrm{las}/{\pi \omega}_0^2$
, with
${E}_\mathrm{las}$
being the energy of the laser pulse. The laser is introduced as a boundary condition and enters into vacuum before interacting with a slab of fused silica at normal incidence. For both the bulk and multi-layer targets, a
$\lambda =800$
nm,
${\tau}_\mathrm{FWHM}=7 fs$
pulse duration, sine-squared pulse with a spot radius of
${\omega}_0=4.65\ \unicode{x3bc}$
m is modeled.

Figure 1 A schematic of the 3D simulations for bulk fused silica (a), and the coating of the multi-layer quarter-wave mirror with a fused
${\mathrm{SiO}}_2$
protective layer (b). The normally incident few-cycle laser pulse (c) is driven from the minimum y boundary into a 0.1 μm vacuum region before the target. The
${\mathrm{SiO}}_2$
regions are represented in yellow and the
${\mathrm{HfO}}_2$
in blue. A cross-section of the multi-layer mirror in the
$x-z$
plane is shown in (d) (the
$z$
dimension is not to scale). The thickness of the top
${\mathrm{SiO}}_2$
layer is 270.90 nm and then there are alternating layers of
${\mathrm{HfO}}_2$
(98.95 nm) and
${\mathrm{SiO}}_2$
(135.45 nm).
3.1 Grid and particle initialization
The simulation setup for bulk
${\mathrm{SiO}}_2$
is shown in Figure 1(a). The simulation box is 1.0 μm in the longitudinal (
$y$
) direction and 9.6 μm in the transverse (
$x$
/
$z$
) directions. The target is 0.9 μm thick, leaving a 0.1 μm vacuum region before the laser interacts with the target. Simple outflow boundaries are used to allow transmission of the laser out of the simulation box with minimal reflection. The bulk silica simulations have a resolution of 5 nm in the longitudinal (
$y$
) direction and 40 nm in the transverse directions. A higher resolution was used in the longitudinal direction to better resolve the ionization dynamics at the interface between the target surface and the vacuum region.
Then we apply this framework to multi-layer interference coatings composed of alternating fused
${\mathrm{SiO}}_2$
(yellow) and
${\mathrm{HfO}}_2$
(blue) layers, as shown in Figures 1(b) and 1(d). The simulation space is 9.6 μm by 1.533 μm by 9.6 μm long in the x, y and z directions, respectively, with a resolution of 13.8 nm in the longitudinal (
$y$
) direction and 40 nm in the transverse directions. The thickness of the surface protective
${\mathrm{SiO}}_2$
layer is
$\lambda$
/(2n), while the rest of the layer thickness is
$\lambda$
/(4n), a typical Bragg quarter-wavelength mirror, where
$n$
is the index of refraction for each material. The top fused
${\mathrm{SiO}}_2$
layer is placed at 0 μm and the pulse enters normally from the y-axis at –0.1 μm.
Both series of simulations are initialized with 1000 neutral
${\mathrm{SiO}}_2$
(or
${\mathrm{HfO}}_2$
) macroparticles per cell with a temperature of 300 K. Similar to Charpin et al.
[
Reference Charpin, Ardaneh, Morel, Giust and Courvoisier
41
], we found that a large number of particles per cell were required for accuracy with the Keldysh ionization model in this regime. The simulations are run to a simulation time of 24 fs, which allows the pulse to make multiple passes across the simulation domain. For each simulation, we use the default time step in EPOCH of 0.95 times the Courant–Friedrichs–Lewy (CFL) limit[
Reference Courant, Friedrichs and Lewy
60
,
Reference Courant, Friedrichs and Lewy
61
], or
$0.95/\left(c\sqrt{1/\Delta {x}^2+1/\Delta {y}^2+1/\Delta {z}^2}\right)$
, where
$\Delta x,\Delta y \text{ and } \Delta z$
represent the grid size in each simulation dimension[
Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers
31
].
3.2 Material properties
The simulations require a number of material properties as inputs to model the interaction and interpret the predictions. These include the linear refractive index, density, and band gap, for which we use standard values listed in Table 1. There is less agreement in reported values for the effective electron and hole masses (
${m}_\mathrm{e}^{\ast }$
and
${m}_\mathrm{h}^{\ast }$
, respectively) and subsequently this results in different reduced effective masses
${m}^{\ast}=1/\left(1/{m}_\mathrm{e}^{\ast }+1/{m}_\mathrm{h}^{\ast}\right)$
. This variation leads to significant differences in predictions for the excited electron density using the Keldysh ionization model[
Reference Zhang, Davenport, Talisa, Smith, Menoni, Gruzdev and Chowdhury
27
,
Reference Gruzdev
65
]. To calculate
${m}_\mathrm{e}^{\ast }$
for fused
${\mathrm{HfO}}_2$
, we assume it is the spherically averaged effective mass around the
$\Gamma$
and B points of the monoclinic
${\mathrm{HfO}}_2$
[
Reference Perevalov, Gritsenko, Erenburg, Badalyan, Wong and Kim
64
,
Reference Zhang, Tripepi, AlShafey, Talisa, Nguyen, Reagan, Sistrunk, Gibson, Alessi and Chowdhury
66
]. We generally use material properties for m-
${\mathrm{HfO}}_2$
as those for amorphous
${\mathrm{HfO}}_2$
are less readily available. These effective electron masses are used for ionized electron particles in the simulations.
Table 1 Material properties used in simulations.

4 Damage modeling of the bulk silica target
We benchmark our framework against the experiment by Chimier et al.
[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
,
Reference Utéza, Sanner, Chimier, Brocas, Varkentina, Sentis, Lassonde, Légaré and Kieffer
21
], which finds damage with a fluence of
$1.18\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
and ablation at
$1.3\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
for a 7 fs FWHM pulse at normal incidence. There is some uncertainty in these thresholds. Other experiments of LIDTs for bulk silica with different experimental conditions including a shorter 5 fs pulse[
Reference Lenzner, Krüger, Sartania, Cheng, Spielmann, Kautek and Krausz
15
,
Reference Kafka, Talisa, Tempea, Austin, Neacsu and Chowdhury
18
] report thresholds from 1.5 to
$1.8\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
. We use a 4.65 μm spot radius in our simulations to match the experimental conditions in the work of Chimier et al.
[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
]. We note that experiments by Lenzner et al.
[
Reference Lenzner, Krüger, Sartania, Cheng, Spielmann, Kautek and Krausz
15
] and Kafka et al.
[
Reference Kafka, Talisa, Tempea, Austin, Neacsu and Chowdhury
18
] used larger spot sizes, although petawatt-class laser systems[
Reference Danson, Haefner, Bromage, Butcher, Chanteloup, Chowdhury, Galvanauskas, Gizzi, Hein, Hillier, Hopps, Kato, Khazanov, Kodama, Korn, Li, Li, Limpert, Ma, Nam, Neely, Papadopoulos, Penman, Qian, Rocca, Shaykin, Siders, Spindloe, Szatmári, Trines, Zhu, Zhu and Zuegel
67
] can often achieve spot sizes of the order of a few wavelengths (e.g., see Ref. [Reference Poole, Willis, Daskalova, George, Feister, Jiang, Snyder, Marketon, Schumacher, Akli, Van Woerkom, Freeman and Chowdhury68]).
4.1 Electron density
For simulation and theoretical work, the predicted excited electron density is often used as a criterion to predict damage. Many studies use the critical electron density[
Reference Chen
69
] for free electrons or some fraction of total ionization[
Reference Werner, Gruzdev, Talisa, Kafka, Austin, Liebig and Chowdhury
70
]. This qualitatively makes sense, as exceeding such thresholds can result in high absorption and subsequent damage. This choice has shown good agreement with longer pulses, although recent work has suggested that this description is insufficient for modeling shorter few-cycle pulses[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
,
Reference Zhokhov and Zheltikov
71
]. As shown in Figure 2, the damage threshold is predicted at about
$0.8\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
by the critical density criterion, which is about a 30% underestimation of the experimental LIDT. Number density is a standard output variable for PIC simulations. It is calculated by mapping the position of the macroparticles to the spatial grid based on the shape function selected for the simulation[
Reference Birdsall and Langdon
29
–
Reference Arber, Bennett, Brady, Lawrence-Douglas, Ramsay, Sircombe, Gillies, Evans, Schmitz, Bell and Ridgers
31
].

Figure 2 The electron density at the center of the x–z plane (averaged over six cells in x and y) along y at 20 fs for a series of PIC simulations at various laser fluences with a 7 fs duration pulse interacting with bulk
${\mathrm{SiO}}_2$
. The critical plasma density
${n}_\mathrm{crit}$
and electron instability density from Ref. [Reference Stampfli and Bennemann72] are labeled with dashed lines. The latter predicts damage around
$1.2\;\mathrm{J}\;{\mathrm{cm}}^{-2}$
.
Alternatively, the instability density suggested by Stampfli and Bennemann[
Reference Stampfli and Bennemann
72
] states that when the conduction band electron density reaches about 9% of the valence band electron density, the elastic shear constant will become negative and the lattice becomes unstable, which then leads directly to a very rapid melting of the crystal structure. This criterion was developed for crystals, but the fundamental physical principles – namely, the relationship between conduction band electron density and the stability of the atomic structure – are similar. By applying this criterion to the bulk fused
${\mathrm{SiO}}_2$
simulations, the damage is achieved at about
$1.2\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, which agrees with the experimental results.
4.2 Energy density
The excited electron energy density criterion has also been suggested to predict damage[ Reference Zhokhov and Zheltikov 71 , Reference Grehn, Seuthe, Höfner, Griga, Theiss, Mermillod-Blondin, Eberstein, Eichler and Bonse 73 ]. The predicted energy density is typically compared to material properties, such as the dissociation energy, or an energy barrier associated with melting or boiling[ Reference Zhokhov and Zheltikov 71 , Reference Wang, Qi, Zhao, Wang and Shao 74 ]. We compare the energy densities in our simulations to these thresholds.
Previous computational approaches typically assume some simple electron energy distribution, whereas in PIC simulation, the energy density can be calculated directly with standard outputs. For our simulations, we multiply the number density by average particle energy for a species in each cell. Alternately, this could be re-sampled to a finer or a coarser grid if the individual macroparticle positions and energies are extracted from the simulation.
Due to variations of reported material properties in the literature and uncertainty of previous simulations, the exact energy density for damage is not agreed upon. For reference, the dissociation energy of
${\mathrm{SiO}}_2$
has reported values from
$54\;\mathrm{to}\;68\ \mathrm{kJ}\;{\mathrm{cm}}^{-3}$
(Refs. [Reference Grehn, Seuthe, Höfner, Griga, Theiss, Mermillod-Blondin, Eberstein, Eichler and Bonse73,Reference Jia, Xu, Li, Feng, Li, Cheng, Sun, Xu and Wang75,Reference Inaba, Fujino and Morinaga76]). For comparison, the threshold for high-energy-density physics[
Reference Drake
77
] is approximately
$100\ \mathrm{kJ}\;{\mathrm{cm}}^{-3}$
.
The energy densities related to melting or boiling are lower, where we can use the temperature-dependent heat capacity and latent heat of vaporization used by Zhao et al.
[
Reference Zhao, Sullivan, Zayac and Bennett
78
,
Reference Zhao, Cheng, Chen, Yuan, Liao, Wang, Liu and Yang
79
] to calculate an energy density of
$5.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
for melting and
$34.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
for boiling. We do have uncertainty in these values. The latent heat of vaporization has the largest contribution to the boiling criteria and there is a great deal of uncertainty in reported values. For example, the calculation above uses values from Bäuerle[
Reference Bäuerle
80
], who calculated the latent heat of vaporization of c-
${\mathrm{SiO}}_2$
to be
$1.23\times {10}^7\;\mathrm{J}\;{\mathrm{kg}}^{-1}$
, while Kraus et al.
[
Reference Kraus, Stewart, Swift, Bolme, Smith, Hamel, Hammel, Spaulding, Hicks, Eggert and Collins
81
] reported
$(1.177\pm 0.095)\times {10}^7\;\mathrm{J}\;{\mathrm{kg}}^{-1}$
and Khmyrov et al.
[
Reference Khmyrov, Grigoriev, Okunkova and Gusarov
82
] used
$0.96\times {10}^7\;\mathrm{J}\;{\mathrm{kg}}^{-1}$
(from Refs. [Reference Grigoriev and Meilikhov83,Reference Samsonov84]). This gives a range from
$28.7$
to
$35.6\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
for boiling. We expect some uncertainty in melting energy density as well.
Figure 3 shows the maximum energy density in simulations with and without collisions for a range of laser fluences. The simulations just including photoionization, (without collisions) have lower energy density at the end of the simulation and do not exceed the boiling criterion until much higher fluences are expected for the LIDT in these conditions.

Figure 3 The peak energy density for the series of PIC simulations at various fluences with a 7 fs duration pulse interacting with bulk fused silica. The experiment[
Reference Chimier, Utéza, Sanner, Sentis, Itina, Lassonde, Légaré, Vidal and Kieffer
16
] being modeled observed damage around
$1.18\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
and ablation[
Reference Utéza, Sanner, Chimier, Brocas, Varkentina, Sentis, Lassonde, Légaré and Kieffer
21
] at
$1.3\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
. The shaded horizontal line/bands indicate approximate energy density thresholds for melting, boiling and dissociation. The shaded vertical bands represent uncertainty in experimental damage and ablation thresholds. The simulations including collisions have a higher predicted energy density. Including both our Keldysh photoionization model and collisional effects shows agreement between the expected damage fluence and the dissociation energy.
In Figure 3, we see that at
$1.3\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, where ablation is observed in experiments, the simulation energy density overlaps with the reported dissociation energy values. For
$1.2\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, near the LIDT, the energy density for the simulation is around
$49\ \mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, exceeding the boiling threshold and near the dissociation energy.
4.3 Kinetic particle motion
The kinetic nature of PIC simulations allows us to explore the energy and motion of the excited electrons. While most previous approaches assume a thermal distribution of the excited electrons, Figure 4 shows that this is not the case during the interaction. The spectra at different times of the simulation are shown and the energy is fitted by the
${\chi}^2$
distribution using the SciPy[
Reference Virtanen, Gommers, Oliphant, Haberland, Reddy, Cournapeau, Burovski, Peterson, Weckesser, Bright, van der Walt, Brett, Wilson, Millman, Mayorov, Nelson, Jones, Kern, Larson, Carey, Polat, Feng, Moore, VanderPlas, Laxalde, Perktold, Cimrman, Henriksen, Quintero, Harris, Archibald, Ribeiro, Pedregosa and van Mulbregt
85
] library:

where
${E}_\mathrm{e}$
is the energy of the electrons and fitting parameters
$k$
,
$\sigma$
are the degree and scale parameters, respectively. The
${\chi}^2$
distribution becomes the standard Maxwell–Boltzmann distribution when
$k$
= 3 and
$\sigma$
=
${k}_\mathrm{b}T$
/2. In our analysis, we primarily focus on the variation of the degrees of freedom
$k$
as it is crucial in describing the main characteristics of the Maxwell–Boltzmann distribution. For both cases, with or without collisions, the excited electrons are highly nonthermal at the early stage of 4 fs, where the degree
$k$
is about 0.7. Then at 10 fs, after the peak intensity passed through the target, the energy spectrum is still nonthermal with k = 1.07 when collisions are not included, as shown in Figure 4(a), while in Figure 4(b) the degree
$k$
reaches 1.86. At the stable stage of 23 fs, the electrons still remain nonthermal, as shown in Figure 4(a), while in Figure 4(b), the
$k$
value is about 2.56, which is approaching the Maxwell–Boltzmann distribution as indicated by the black curve giving the average energy at 23 fs. Therefore, our simulations not only show the dynamic evolution of the excited electron energy spectrum during the interaction but also show the importance of including collisions to capture particle dynamics. When collisions are included in the simulations, the energy absorbed by the electrons increases, leading to higher average energy (Figure 4) and higher maximum energy density (Figure 3) at the end of the simulation.

Figure 4 Excited electron energy distributions in a
$200\ \mathrm{nm} \times 200\ \mathrm{nm} \times 900\ \mathrm{nm}$
region of the target at the center of the interaction for a
$1.2\;\mathrm{J}\;{\mathrm{cm}}^{-2}$
pulse. A simulation with just field ionization is shown in (a) and a simulation with field ionization and collisions is shown in (b). A fit with the given temperature is shown. We observe the nonthermal nature, especially for early times and for simulations without collisions.
5 Damage modeling of multi-layer mirrors
For the multi-layer dielectric mirror,
${\mathrm{HfO}}_2$
is expected to have a lower damage threshold than
${\mathrm{SiO}}_2$
[
Reference Mero, Liu, Rudolph, Ristau and Starke
86
,
Reference Wang, Qi, Zhao, Wang and Shao
87
] due to the lower bandgap. Therefore, the damage may be initiated in the first
${\mathrm{HfO}}_2$
layer. For example, Talisa et al.
[
Reference Talisa, Alshafey, Tripepi, Krebs, Davenport, Randel, Menoni and Chowdhury
22
] found the damage threshold for a four-layer
${\mathrm{SiO}}_2$
/
${\mathrm{HfO}}_2$
mirror to be half that of a bulk
${\mathrm{SiO}}_2$
target. Due to the high computational cost of 3D simulations and uncertainty in material properties for
${\mathrm{HfO}}_2$
, we explore a simple mirror with a relatively small spot size to gain a better qualitative understanding of the interaction. Future validations with experiments should be coupled with more accurate material property measurements and LIDT measurements of bulk
${\mathrm{HfO}}_2$
.
5.1 Electron density
As mentioned in Section 4, the plasma critical density may underestimate the damage threshold; it predicts the LIDT slightly above
$0.2\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
in the first
${\mathrm{HfO}}_2$
layer, as shown in Figure 5. If we apply the instability density criterion to the mirror target, the damage may be achieved at about
$0.5\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
in the same layer. Above
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, the hot spots in the top two layers are almost fully ionized as the peak electron density reaches about
$1.5\times {10}^{22}\ {\mathrm{cm}}^{-3}$
.

Figure 5 The electron density at the center of the x–z plane along y at 20 fs for a series of PIC simulations at various fluences with a 7 fs duration pulse interacting with a multi-layer mirror. The yellow area is
${\mathrm{SiO}}_2$
and blue area is
${\mathrm{HfO}}_2$
. Different critical electron densities are labeled in the figure with dashed lines.
5.2 Energy density
The peak electron energy density along y at the center of the x–z plane in each layer with different fluences is shown in Figure 6. The dissociation energy of fused
${\mathrm{HfO}}_2$
has not been extensively studied, particularly for amorphous samples, which are expected in these multi-layer coatings. There are reported values for the formation energy density of m-
${\mathrm{HfO}}_2$
from 48.44 to
$52.64\ \mathrm{kJ}\;{\mathrm{cm}}^{-3}$
[
Reference Wang, Zinkevich and Aldinger
88
–
Reference Panish and Reif
94
], which are similar to the dissociation energy density of
${\mathrm{SiO}}_2$
. We assume it has the same value as fused
${\mathrm{SiO}}_2$
since both of their molecules have four valence band electrons and are amorphous[
Reference Zhang, Menoni, Gruzdev and Chowdhury
28
], which is about
$54\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, indicated by the red dashed line. In addition, the effects of structure on boiling and melting points are not considered, as each layer is assumed to retain the melting and boiling points characteristic of its bulk state. This criterion suggests the damage should occur at the surface at fluence of about
$1.4\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, which is even higher than the LIDT of bulk fused
${\mathrm{SiO}}_2$
discussed in Section 4.

Figure 6 The peak electron energy density at the center of the x–z plane along y at 20 fs for a series of PIC simulations at various fluences with a 7 fs duration pulse interacting with a multi-layer mirror. The yellow area is fused
${\mathrm{SiO}}_2$
and blue area is fused
${\mathrm{HfO}}_2$
. The
${\mathrm{SiO}}_2$
boiling and melting energy densities are at about
$34.7$
and
$5.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, respectively, indicated as yellow dashed lines, and the
${\mathrm{HfO}}_2$
boiling and melting energy densities are at about
$49.9$
and
$10.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, respectively, indicated as blue dashed lines.
Instead let us consider energy density thresholds for melting and boiling, as these may relate to damage within the layers of a coating. To calculate the melting and boiling energy densities, we make the following assumptions: (i) the vaporization latent heat for
${\mathrm{HfO}}_2$
is the same as that of
${\mathrm{SiO}}_2$
and (ii) the heat capacity for fused
${\mathrm{HfO}}_2$
is the same as that of m-
${\mathrm{HfO}}_2$
[
Reference Low, Paulson, D’Mello and Stan
95
]. These approximations give the boiling energy density of
${\mathrm{HfO}}_2$
to be about
$49.9\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, which is much higher than that of
${\mathrm{SiO}}_2$
,
$34.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, and may overestimate the actual damage threshold. Similarly, the melting energy density of
${\mathrm{SiO}}_2$
is about
$5.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
, and we calculated about
$10.7\;\mathrm{kJ}\;{\mathrm{cm}}^{-3}$
for
${\mathrm{HfO}}_2$
.
The boiling energy density criterion predicts the LIDT at a fluence between
$1.1$
and
$1.2\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
on the surface of the mirror, which is close to the LIDT of bulk
${\mathrm{SiO}}_2$
. Alternately, applying the melting energy density criterion gives the LIDT to be less than
$0.5\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, and the damage site is initiated in the first
${\mathrm{HfO}}_2$
layer, as expected.
5.3 Plasma screening effects
As shown in Figure 6, the global maximum energy density at low fluences is in the first
${\mathrm{HfO}}_2$
layer, as expected. When the fluence exceeds about
$1.1\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
it shifts to the top
${\mathrm{SiO}}_2$
layer and the
${\mathrm{HfO}}_2$
energy density increases slowly after the fluence reaches about
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
. This is because the excited electron density in the first
${\mathrm{SiO}}_2$
layer begins to exceed the critical plasma density (Figure 5). In the top
${\mathrm{SiO}}_2$
layer, the steady state of the dynamic simulation leads to a local maximum enhancement of the electric field at the center.
As the fluence increases beyond
$0.5\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
, we observe a new peak appearing and shifting from the center to the surface in both the electron and energy density profiles. The ionization rate at the center of the top
${\mathrm{SiO}}_2$
layer is enhanced due to the strong intensity and thus the electron density reaches a maximum. The absorption and reflection will continue to increase so that the source can hardly penetrate the target by the end of the laser pulse. Therefore, the resonant pattern of the electric field is altered, and a new intensity peak appears.
To illustrate this process clearly, a two-dimensional y–z cross-section of the layers at the center of the x-axis is shown in Figure 7 at fluence of
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
. The maximum electron and energy densities are both observed in the first
${\mathrm{HfO}}_2$
layer, as shown in Figures 7(a) and 7(b), while the maximum accumulated intensity is in the first
${\mathrm{SiO}}_2$
layer in Figure 7(c). The intensity is recorded at intervals of 0.5 fs and compared across all time points to generate this figure. These results are similar to those from the FDTD simulations by Zhang et al.
[
Reference Zhang, Menoni, Gruzdev and Chowdhury
28
].

Figure 7 The (a) energy density and (b) electron density at the center of x on the y–z plane at 20 fs and
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
with a 7 fs duration pulse interacting with a multi-layer mirror. The mirror surface starts at y = 0, and the dashed red lines are the interfaces between layers. The white areas have no excited electrons. (c) The maximum accumulated normalized intensity over the entire simulation.
Noticeably, the electron density, energy density and intensity profiles in the protective
${\mathrm{SiO}}_2$
layer all present a bump extending from the center to the surface, which is different from in previous FDTD simulation works by Zhang et al.
[
Reference Zhang, Menoni, Gruzdev and Chowdhury
28
]. We observed that the electron density peak first appeared at the center and then expanded at about 9 fs to the surface, and became stable at about 15 fs. These are the times when the pulse peak intensity reached the surface and when the entire pulse left the surface.
Furthermore, in Figure 6, the first
${\mathrm{HfO}}_2$
layer is more strongly affected by the lower fluence pulse. At higher fluence, there is significant growth of the energy density at the surface, while the lower layers do not show a significant increase. In addition, the energy density at
$0.9\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
is the highest among all the fluences, which adds additional evidence that the first
${\mathrm{SiO}}_2$
layer has reflected more injected energy due to the plasma screening effects.
The plasma generation could also imply the breakdown threshold, which is defined as a permanent change to the optical properties. We can see from both the energy and electron density profiles that the local peak in the top
${\mathrm{SiO}}_2$
layer starts to shift at
$0.5\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
and the profile is no longer symmetric at the center. This analysis is also consistent with the conclusion predicted by the instability criterion.
5.4 Particle energy
The kinetic nature of the excited electrons is shown in Figure 8. The spectrum of the electrons in the
${\mathrm{SiO}}_2$
layer is wider than that in the
${\mathrm{HfO}}_2$
layer, although both electron density and energy density reach maximum in the first
${\mathrm{HfO}}_2$
layer. For both layers, three snapshots of the energy distribution are shown. At 6 fs, in the early stage of the interaction, there are some excited electrons generated from the ionization. The electrons are highly nonthermalized since the degree
$k$
is less than one. At about 10 fs, the peak intensity has passed through the target, leading to average electron energies of 24.56 and 8.61 eV for the
${\mathrm{SiO}}_2$
and
${\mathrm{HfO}}_2$
layers, respectively. The electrons are still nonthermalized as
$k$
is about 1.4. At 24 fs, the pulse front has left the target for about 10 fs. The degree
$k$
is above 2 and approaching 3, indicating the thermalization process is finishing towards a Maxwell–Boltzmann distribution, indicated by the black curves in Figure 8.

Figure 8 The energy histogram for the electrons in the first
${\mathrm{SiO}}_2$
layer (a) and
${\mathrm{HfO}}_2$
layer (b) at 6, 10, 24 fs for the
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
simulation with
${\chi}^2$
distribution fitted. The black curve is the Maxwell–Boltzmann distribution given the average energy at the stable stage around 24 fs.
5.5 Particle dynamics
The peak intensity of the
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
pulse is about
${10}^{14}\ \mathrm{W}\;{\mathrm{cm}}^{-2}$
, giving a theoretical ponderomotive energy[
Reference Geoffroy, Duchateau, Fedorov, Martin and Guizard
96
]
${U}_\mathrm{p}$
of approximately 10 and 5.5 eV for
${\mathrm{SiO}}_2$
and
${\mathrm{HfO}}_2$
electrons, respectively (using the
${m}_\mathrm{e}^{\ast }$
values from Table 1). Some studies have reported keV-scale electron spectra under similar intensities but with longer pulse durations[
Reference Geoffroy, Duchateau, Fedorov, Martin and Guizard
96
]. This enhancement is often attributed to the surface modification of the target due to ablation, which enhances the local intensity and facilitates stronger electron acceleration. In our previous work, we also observed keV-scale spectra using a 7 fs pulse by introducing pit-like defects on the target surface under comparable conditions[
Reference Smith, Zhang, Gruzdev and Chowdhury
40
].
In gases, electrons with kinetic energies exceeding
$10{U}_\mathrm{p}$
are typically observed[
Reference Paulus, Becker, Nicklich and Walther
97
], and in our simulation, the highest electron energy from the first
${\mathrm{SiO}}_2$
layer reaches 150 eV. In addition, a few electrons in the first
${\mathrm{HfO}}_2$
layer achieve energies around 100 eV, as shown in Figure 9 (left). These results align with the kinetic energy predictions for excited electrons based on the Drude model. If we assume the collisional frequency is
$1\ {\mathrm{fs}}^{-1}$
, then the energy would be about 118 and 65 eV for
${\mathrm{SiO}}_2$
and
${\mathrm{HfO}}_2$
, respectively, as seen in the studies by Duchateau et al.
[
Reference Duchateau, Geoffroy, Dyan, Piombini and Guizard
98
]. Furthermore, the excited
${\mathrm{SiO}}_2$
electrons at the
${\mathrm{HfO}}_2$
–
${\mathrm{SiO}}_2$
interfaces rarely enter the
${\mathrm{HfO}}_2$
layers. There are a limited number of
${\mathrm{HfO}}_2$
electrons penetrating into the adjacent
${\mathrm{SiO}}_2$
layers for a few tens of nanometers.

Figure 9 The
${\mathrm{SiO}}_2$
and
${\mathrm{HfO}}_2$
electron energy spatial distribution for a fluence of
$0.7\ \mathrm{J}\;{\mathrm{cm}}^{-2}$
is shown on the left-hand side at 24 fs. The highest energy particles are found in the center of the first layer and at the interfaces between layers. Particle trajectories for random 2% of electrons with low (below average) final energy demonstrate the ionization dynamics in the first
${\mathrm{SiO}}_2$
layer up to 16 fs (after the pulse has left). Ionization near the surface generally occurs at later times, as indicated by the color of the tracks.
The tracks of select electrons in the first
${\mathrm{SiO}}_2$
layer are shown from 6 to 16 fs in Figure 9 (right). Most electrons near the vacuum interface are born after 10 fs, indicated by their yellowish tails. In contrast, electrons near the center of the first
${\mathrm{SiO}}_2$
layer are born earlier. This suggests that the electron density expansion in Figures 5 and 7(b) is due to direct excitation near the surface rather than displacement from the center.
6 Conclusion
Understanding few-cycle pulse interactions with dielectric optical components and their LIDT is essential for advancing next-generation laser systems. The use of kinetic simulations in relevant regimes is becoming increasingly popular[ Reference Mitchell, Schumacher and Chowdhury 33 , Reference Déziel, Dubé, Messaddeq, Messaddeq and Varin 36 , Reference Ding, Lim, Do, Xiong, Mahfoud, Png, Bosman, Ang and Wu 37 , Reference Charpin, Ardaneh, Morel, Giust and Courvoisier 41 ]. Kinetic simulations allow us to capture the nonthermal nature of the initial interaction, which is important to accurately model absorption and ionization. We show that both excited electron density and energy density provide insight into the LIDT. Our framework shows good agreement with experimental LIDTs for bulk silica targets. Multi-layer mirror simulations indicate that plasma screening effects can alter the laser interaction and electron energy distribution for high fluences.
In the future, this framework can be applied to a variety of mirror and grating designs[ Reference Zhang, Menoni, Gruzdev and Chowdhury 28 ] for both near- and mid-infrared wavelengths[ Reference Shcherbakov, Sartorello, Zhang, Bocanegra, Bosch, Tripepi, Talisa, AlShafey, Smith, Londo, Légaré, Chowdhury and Shvets 39 , Reference Austin, Kafka, Lai, Wang, Blaga and Chowdhury 99 ]. In addition, simulations can be inserted into an optimization algorithm to optimize the LIDT or other properties of interest for optical components[ Reference Smith, Orban, Morrison, George, Ngirmang, Chowdhury and Roquemore 100 ]. This framework already provides a deeper qualitative understanding of the dynamics of laser damage, and we show promising quantitative agreement with experiments for few-cycle pulses. Further development of this framework, including impact ionization[ Reference Charpin, Ardaneh, Morel, Giust and Courvoisier 41 , Reference Morris, Goffrey, Bennett and Arber 54 ], coupled with more precise measurements of material properties for relevant optical coating designs, can allow further validation of the framework across large ranges of laser fluence.
Appendix A: Keldysh ionization model implementation
Here we include additional implementation details for our Keldysh ionization model in EPOCH. As stated in Section 2.1, we use 500 terms in the infinite sum in Equation (3). To decide the number of terms, we tested the convergence as shown in Figure 10. Using 100 terms yields errors below 1% for our laser intensities, while selecting 500 terms provided results nearly identical to those for 1000 terms.

Figure 10 Percent error for 10–500 terms in the infinite sum in Equation (3) for
${\mathrm{SiO}}_2$
at varying laser intensities. The error is calculated with the result for 1000 terms as the ‘actual’ value. Our simulation framework uses 500 terms, which shows a negligible difference compared to 1000 terms for these intensities. The errors for
${\mathrm{HfO}}_2$
(not shown) are of similar magnitude to those for
${\mathrm{SiO}}_2$
.
The Dawson integral (
$\Phi$
in Equation (3)) is evaluated using John Burkardt’s Fortran 90 code[
101
], which is based on the work by Cody et al.
[
Reference Cody, Paciorek and Thacher
102
].
Acknowledgements
This research was funded by DOE STTR grant No. DE-SC0019900. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award SBIR-ERCAP0021422. This project also utilized resources from the Ohio Supercomputer Center[ 103 ]. The code EPOCH used in this work was in part funded by the UK EPSRC grants EP/G054950/1, EP/G056803/1, EP/G055165/1 and EP/M022463/1. This work used matplotlib[ Reference Hunter 104 ], SciPy[ Reference Virtanen, Gommers, Oliphant, Haberland, Reddy, Cournapeau, Burovski, Peterson, Weckesser, Bright, van der Walt, Brett, Wilson, Millman, Mayorov, Nelson, Jones, Kern, Larson, Carey, Polat, Feng, Moore, VanderPlas, Laxalde, Perktold, Cimrman, Henriksen, Quintero, Harris, Archibald, Ribeiro, Pedregosa and van Mulbregt 85 ] and NumPy[ Reference Harris, Millman, van der Walt, Gommers, Virtanen, Cournapeau, Wieser, Taylor, Berg, Smith, Kern, Picus, Hoyer, van Kerkwijk, Brett, Haldane, del Río, Wiebe, Peterson, Gérard-Marchant, Sheppard, Reddy, Weckesser, Abbasi, Gohlke and Oliphant 105 ] to graph and analyze the simulation results.