Semiconductor Manufacturing: Etch Equipment Mathematical Modeling
1. Introduction
Plasma etching is a critical process in semiconductor manufacturing where material is selectively removed from wafer surfaces using reactive plasmas. Mathematical modeling spans multiple scales and physics domains:
- Plasma physics — Generation and transport of reactive species
- Surface chemistry — Reaction kinetics at the wafer surface
- Transport phenomena — Gas flow, heat transfer, species diffusion
- Feature evolution — Nanoscale profile development
- Process control — Run-to-run optimization and fault detection
1.1 Etch Process Types
| Type | Mechanism | Selectivity | Anisotropy |
|------|-----------|-------------|------------|
| Wet Etch | Chemical dissolution | High | Isotropic |
| Plasma Etch | Ion + radical reactions | Medium-High | Anisotropic |
| RIE | Ion-enhanced chemistry | Medium | High |
| ICP-RIE | High-density plasma | Tunable | Very High |
| ALE | Self-limiting cycles | Very High | Atomic-level |
2. Plasma Discharge Modeling
2.1 Electron Kinetics
The electron energy distribution function (EEDF) governs ionization and dissociation rates. It is described by the Boltzmann transport equation:
$$
\frac{\partial f}{\partial t} + \vec{v} \cdot
abla_r f + \frac{e\vec{E}}{m_e} \cdot
abla_v f = C[f]
$$
Where:
- $f(\vec{r}, \vec{v}, t)$ — Electron distribution function
- $\vec{E}$ — Electric field vector
- $m_e$ — Electron mass
- $C[f]$ — Collision integral
Two-Term Approximation
For weakly anisotropic distributions:
$$
f(\vec{r}, \vec{v}, t) = f_0(\vec{r}, v, t) + \vec{v} \cdot \vec{f}_1(\vec{r}, v, t)
$$
2.2 Species Continuity Equations
For each species $i$ (electrons, ions, neutrals, radicals):
$$
\frac{\partial n_i}{\partial t} +
abla \cdot \vec{\Gamma}_i = S_i
$$
Where:
- $n_i$ — Number density of species $i$ (m⁻³)
- $\vec{\Gamma}_i$ — Flux vector (m⁻² s⁻¹)
- $S_i$ — Source/sink term from reactions (m⁻³ s⁻¹)
Flux Expressions
- Neutral species (diffusion only):
$$
\vec{\Gamma}_n = -D_n
abla n_n
$$
- Charged species (drift-diffusion):
$$
\vec{\Gamma}_{\pm} = \pm \mu_{\pm} n_{\pm} \vec{E} - D_{\pm}
abla n_{\pm}
$$
Where:
- $D$ — Diffusion coefficient (m² s⁻¹)
- $\mu$ — Mobility (m² V⁻¹ s⁻¹)
Einstein Relation
$$
D = \frac{\mu k_B T}{e}
$$
2.3 Reaction Rate Coefficients
Rate coefficients are computed by integrating cross-sections over the EEDF:
$$
k = \int_0^{\infty} \sigma(\varepsilon) \cdot v(\varepsilon) \cdot f(\varepsilon) \, d\varepsilon
$$
Where:
- $\sigma(\varepsilon)$ — Energy-dependent cross-section (m²)
- $v(\varepsilon) = \sqrt{2\varepsilon/m_e}$ — Electron velocity
- $f(\varepsilon)$ — Normalized EEDF
Key Reactions in Fluorine-Based Plasmas
| Reaction | Type | Rate Expression |
|----------|------|-----------------|
| $e + SF_6 \rightarrow SF_5^+ + F + 2e$ | Ionization | $k_1(T_e)$ |
| $e + SF_6 \rightarrow SF_5 + F + e$ | Dissociation | $k_2(T_e)$ |
| $e + SF_6 \rightarrow SF_6^- $ | Attachment | $k_3(T_e)$ |
| $F + Si \rightarrow SiF_{(ads)}$ | Adsorption | $s \cdot \Gamma_F$ |
2.4 Electron Energy Balance
$$
\frac{\partial}{\partial t}\left(\frac{3}{2} n_e k_B T_e\right) +
abla \cdot \vec{q}_e = P_{abs} - P_{loss}
$$
Where:
- $P_{abs}$ — Power absorbed from RF field (W m⁻³)
- $P_{loss}$ — Power lost to collisions (W m⁻³)
$$
P_{loss} = \sum_j n_e n_j k_j \varepsilon_j
$$
2.5 Electromagnetic Field Equations
Capacitively Coupled Plasma (CCP)
Poisson's equation:
$$
abla^2 \phi = -\frac{\rho}{\varepsilon_0} = -\frac{e(n_i - n_e)}{\varepsilon_0}
$$
Inductively Coupled Plasma (ICP)
Wave equation for the azimuthal electric field:
$$
abla^2 E_\theta - \frac{1}{c^2}\frac{\partial^2 E_\theta}{\partial t^2} = \mu_0 \frac{\partial J_\theta}{\partial t}
$$
With plasma conductivity:
$$
\sigma_p = \frac{n_e e^2}{m_e(
u_m + i\omega)}
$$
3. Sheath Physics
The plasma sheath is a thin, ion-rich region at the wafer surface that accelerates ions for bombardment.
3.1 Bohm Criterion
Ions must reach the sheath edge with minimum velocity:
$$
v_B = \sqrt{\frac{k_B T_e}{M_i}}
$$
Where:
- $k_B$ — Boltzmann constant (1.38 × 10⁻²³ J K⁻¹)
- $T_e$ — Electron temperature (K or eV)
- $M_i$ — Ion mass (kg)
3.2 Child-Langmuir Law
Maximum ion current density through a collisionless sheath:
$$
J_{CL} = \frac{4\varepsilon_0}{9}\sqrt{\frac{2e}{M_i}} \cdot \frac{V_s^{3/2}}{d^2}
$$
Where:
- $V_s$ — Sheath voltage (V)
- $d$ — Sheath thickness (m)
- $\varepsilon_0$ — Permittivity of free space (8.85 × 10⁻¹² F m⁻¹)
3.3 Sheath Thickness
Approximate expression:
$$
d \approx \lambda_D \cdot \left(\frac{2eV_s}{k_B T_e}\right)^{3/4}
$$
Where Debye length:
$$
\lambda_D = \sqrt{\frac{\varepsilon_0 k_B T_e}{n_e e^2}}
$$
3.4 Ion Energy Distribution Function (IEDF)
The IEDF depends critically on the ratio:
$$
\xi = \frac{\tau_{ion}}{\tau_{RF}} = \frac{\omega_{RF} \cdot d}{v_B}
$$
Where:
- $\xi \gg 1$ (high frequency): Ions see time-averaged sheath voltage → narrow IEDF
- $\xi \ll 1$ (low frequency): Ions respond to instantaneous voltage → bimodal IEDF
Bimodal IEDF Expression
For RF sheaths:
$$
f(E) \propto \frac{1}{\sqrt{(E - E_{min})(E_{max} - E)}}
$$
With:
- $E_{max} = e(V_{dc} + V_{rf})$
- $E_{min} = e(V_{dc} - V_{rf})$
3.5 Collisional Sheath Effects
When $d > \lambda_{mfp}$ (ion mean free path), ion-neutral collisions broaden the IEDF:
$$
f(E) \propto E \cdot \exp\left(-\frac{E}{\bar{E}}\right)
$$
4. Surface Reaction Kinetics
4.1 General Etch Rate Model
$$
ER = \underbrace{Y_{phys}(E,\theta) \cdot \Gamma_{ion}}_{\text{Physical sputtering}} + \underbrace{Y_{chem} \cdot \Gamma_R \cdot \theta_{ads} \cdot f(E_{ion})}_{\text{Ion-enhanced chemistry}}
$$
Where:
- $ER$ — Etch rate (nm min⁻¹ or Å s⁻¹)
- $Y_{phys}$ — Physical sputtering yield (atoms/ion)
- $Y_{chem}$ — Chemical etch yield coefficient
- $\Gamma$ — Flux (m⁻² s⁻¹)
- $\theta_{ads}$ — Surface coverage fraction (0–1)
- $f(E_{ion})$ — Ion enhancement function
4.2 Physical Sputtering Yield
Sigmund Theory
For normal incidence:
$$
Y_0(E) = \frac{3\alpha}{4\pi^2 U_s} \cdot \frac{4M_1 M_2}{(M_1 + M_2)^2} \cdot E
$$
Where:
- $U_s$ — Surface binding energy (eV)
- $M_1$, $M_2$ — Ion and target atom masses
- $\alpha$ — Dimensionless parameter (~0.2–0.4)
Threshold Energy
Sputtering occurs only above threshold:
$$
E_{th} \approx \frac{(M_1 + M_2)^2}{4M_1 M_2} \cdot U_s
$$
4.3 Angular Dependence of Sputtering
Yamamura formula:
$$
Y(\theta) = Y_0 \cdot \cos^{-f}(\theta) \cdot \exp\left[-b\left(\frac{1}{\cos\theta} - 1\right)\right]
$$
Where:
- $\theta$ — Ion incidence angle from surface normal
- $f$ — Fitting parameter (~1.5–2.5)
- $b$ — Fitting parameter (~0.1–0.5)
Physical interpretation:
- $\cos^{-f}(\theta)$ term: Enhanced yield at grazing angles (energy deposited closer to surface)
- $\exp[-b(\cdot)]$ term: Suppression at very grazing angles (reflection)
4.4 Surface Coverage Dynamics
Langmuir adsorption kinetics:
$$
\frac{d\theta}{dt} = \underbrace{s \cdot \Gamma_R (1-\theta)}_{\text{Adsorption}} - \underbrace{k_d \cdot \theta}_{\text{Thermal desorption}} - \underbrace{k_{react} \cdot \theta \cdot \Gamma_{ion}}_{\text{Ion-induced reaction}}
$$
Where:
- $s$ — Sticking coefficient (0–1)
- $k_d =
u_0 \exp(-E_d/k_B T)$ — Desorption rate
- $
u_0$ — Attempt frequency (~10¹³ s⁻¹)
- $E_d$ — Desorption activation energy (eV)
Steady-State Coverage
$$
\theta_{ss} = \frac{s \cdot \Gamma_R}{s \cdot \Gamma_R + k_d + k_{react} \cdot \Gamma_{ion}}
$$
4.5 Ion-Enhanced Etching Mechanisms
Damage Model
Ion bombardment creates reactive sites:
$$
ER = k \cdot [\text{Damage}] \cdot \Gamma_R
$$
$$
[\text{Damage}] = \frac{Y_d \cdot \Gamma_{ion}}{k_{anneal} + k_{react} \cdot \Gamma_R}
$$
Chemically Enhanced Physical Sputtering
Product species have lower binding energy:
$$
Y_{eff} = Y_{substrate} \cdot (1 - \theta) + Y_{product} \cdot \theta
$$
Where typically $Y_{product} > Y_{substrate}$.
4.6 Silicon Etching in Fluorine Plasmas
Simplified mechanism:
1. Adsorption: $F_{(g)} + Si^* \rightarrow SiF_{(ads)}$
2. Fluorination: $SiF_{(ads)} + F \rightarrow SiF_2 \rightarrow SiF_3 \rightarrow SiF_4$
3. Desorption: $SiF_4 \xrightarrow{ion} SiF_4 (g)\uparrow$
Etch rate expression:
$$
ER_{Si} = \frac{N_0}{\rho_{Si}} \left[ k_s \cdot \Gamma_F \cdot \theta_F + Y_{ion} \cdot \Gamma_{ion} \right]
$$
Where:
- $N_0$ — Avogadro's number
- $\rho_{Si}$ — Silicon atomic density (5 × 10²² cm⁻³)
4.7 Oxide Etching in Fluorocarbon Plasmas
More complex due to polymer competition:
$$
ER_{ox} = k_{etch} \cdot \Gamma_{ion} \cdot E_{ion}^n \cdot \exp\left(-\frac{t_{poly}}{t_0}\right)
$$
Where:
- $t_{poly}$ — Polymer thickness
- Balance between etching and deposition determines regime
Regime boundaries:
- High F/C ratio → Etching dominant
- Low F/C ratio → Deposition dominant (polymerization)
5. Feature-Scale Modeling
5.1 Level Set Method
The surface is represented implicitly as the zero level set of $\phi(\vec{x}, t)$:
$$
\phi(\vec{x}, t) = \begin{cases}
< 0 & \text{inside material} \\
= 0 & \text{surface} \\
> 0 & \text{outside (plasma/vacuum)}
\end{cases}
$$
Evolution Equation
$$
\frac{\partial \phi}{\partial t} + V_n |
abla \phi| = 0
$$
Where $V_n$ is the velocity in the normal direction:
$$
\vec{n} = \frac{
abla \phi}{|
abla \phi|}
$$
Advantages
- Handles topological changes naturally (merging, splitting)
- No explicit surface tracking required
- Curvature easily computed: $\kappa =
abla \cdot \vec{n}$
5.2 Flux Calculation at Surface Points
Local etch velocity depends on incident fluxes:
$$
V_n(\vec{x}) = \Omega \cdot \left[ Y_{phys} \cdot \Gamma_{ion}(\vec{x}) + Y_{chem} \cdot \Gamma_R(\vec{x}) \cdot \theta(\vec{x}) \right]
$$
Where $\Omega$ is the atomic volume.
5.3 Knudsen Transport in High Aspect Ratio Features
At low pressure, neutral mean free path > feature dimensions → free molecular flow.
View Factor Method
Flux at surface point P:
$$
\Gamma(P) = \Gamma_0 \cdot \Omega(P) + \int_{\text{visible}} \Gamma(P') \cdot K(P', P) \, dA'
$$
Where:
- $\Gamma_0$ — Flux from plasma (at feature opening)
- $\Omega(P)$ — Solid angle subtended by opening at P
- $K(P', P)$ — Kernel for re-emission from P' to P
Cosine Re-emission Law
For diffuse reflection:
$$
K(P', P) = \frac{\cos\theta' \cos\theta}{\pi r^2} \cdot (1 - s)
$$
Where:
- $\theta'$, $\theta$ — Angles from surface normals
- $r$ — Distance between points
- $s$ — Sticking coefficient
5.4 Clausing Factor for Tubes
Transmission probability through a cylindrical hole:
$$
W = \frac{1}{1 + \frac{3L}{8r}}
$$
Where $L$ = length, $r$ = radius.
For aspect ratio $AR = L/(2r)$:
$$
W \approx \frac{1}{1 + \frac{3}{4}AR}
$$
5.5 Aspect Ratio Dependent Etching (ARDE)
Empirical model:
$$
\frac{ER(AR)}{ER_0} = \frac{1}{1 + \beta \cdot AR^n}
$$
Where:
- $ER_0$ — Etch rate at open area
- $\beta$, $n$ — Fitting parameters (typically $n \approx 1$–2)
Physical causes:
- Neutral transport limitation (Knudsen diffusion)
- Ion angular distribution effects
- Charging effects in dielectric etching
5.6 Ion Angular Distribution Effects
Ions have finite angular spread due to:
- Thermal velocity at sheath edge
- Collisions in sheath
- Non-vertical electric fields
Distribution often modeled as:
$$
f(\theta_{ion}) = \frac{1}{\sqrt{2\pi}\sigma_\theta} \exp\left(-\frac{\theta_{ion}^2}{2\sigma_\theta^2}\right)
$$
Typical $\sigma_\theta \approx 2°$–5°
5.7 Monte Carlo Feature-Scale Methods
Algorithm:
1. Launch particle from plasma with appropriate energy/angle distribution
2. Track trajectory to surface
3. Evaluate reaction probability based on local conditions
4. If reaction occurs, remove material; else reflect particle
5. Repeat for statistical convergence
6. Advance surface based on accumulated removal
Advantages:
- Naturally handles stochastic effects
- Easy to incorporate complex physics
- Parallelizable
6. Equipment-Scale Transport
6.1 Gas Flow Regimes
Characterized by Knudsen number:
$$
Kn = \frac{\lambda}{L}
$$
Where $\lambda$ is mean free path, $L$ is characteristic length.
| Kn Range | Regime | Model |
|----------|--------|-------|
| $< 0.01$ | Continuum | Navier-Stokes |
| $0.01$–$0.1$ | Slip flow | Modified N-S |
| $0.1$–$10$ | Transitional | DSMC |
| $> 10$ | Free molecular | Kinetic theory |
6.2 Navier-Stokes Equations
Continuity:
$$
\frac{\partial \rho}{\partial t} +
abla \cdot (\rho \vec{v}) = 0
$$
Momentum:
$$
\rho \left( \frac{\partial \vec{v}}{\partial t} + \vec{v} \cdot
abla \vec{v} \right) = -
abla p + \mu
abla^2 \vec{v} + \frac{\mu}{3}
abla(
abla \cdot \vec{v})
$$
Energy:
$$
\rho c_p \left( \frac{\partial T}{\partial t} + \vec{v} \cdot
abla T \right) =
abla \cdot (k
abla T) + \Phi + Q_{source}
$$
Where $\Phi$ is viscous dissipation.
6.3 Slip Boundary Conditions
For Knudsen numbers 0.01–0.1:
$$
v_{slip} = \frac{2 - \sigma_v}{\sigma_v} \lambda \left. \frac{\partial v}{\partial n} \right|_{wall}
$$
$$
T_{slip} - T_{wall} = \frac{2 - \sigma_T}{\sigma_T} \frac{2\gamma}{\gamma + 1} \frac{\lambda}{Pr} \left. \frac{\partial T}{\partial n} \right|_{wall}
$$
Where $\sigma_v$, $\sigma_T$ are accommodation coefficients.
6.4 Wafer Temperature Model
Energy balance at wafer surface:
$$
\rho c_p t_w \frac{\partial T_w}{\partial t} = Q_{ion} + Q_{chem} - Q_{rad} - Q_{cond}
$$
Components:
- Ion bombardment: $Q_{ion} = \Gamma_{ion} \cdot E_{ion}$
- Chemical reactions: $Q_{chem} = \Gamma_{etch} \cdot \Delta H_{rxn}$
- Radiation: $Q_{rad} = \varepsilon \sigma (T_w^4 - T_{wall}^4)$
- Conduction to chuck: $Q_{cond} = h_c (T_w - T_{chuck})$
The contact conductance $h_c$ depends on:
- Backside gas pressure
- Surface roughness
- Clamping force
6.5 Uniformity Modeling
Radial etch rate profile:
$$
ER(r) = ER_0 \cdot \left[ 1 + \sum_{n=1}^{N} a_n \left( \frac{r}{R_w} \right)^{2n} \right]
$$
Where $R_w$ is wafer radius.
Uniformity metric:
$$
\text{Uniformity} = \frac{ER_{max} - ER_{min}}{2 \cdot ER_{avg}} \times 100\%
$$
6.6 Loading Effect
Etch rate depends on exposed area:
$$
ER = \frac{ER_0}{1 + \beta \cdot A_{exposed}}
$$
Or in terms of pattern density $\rho_p$:
$$
ER(\rho_p) = ER_0 \cdot \frac{1 - \rho_p}{1 - \rho_p + \rho_p \cdot \frac{ER_0}{ER_{max}}}
$$
7. Multiscale Coupling
7.1 Scale Hierarchy
| Scale | Dimension | Time | Physics |
|-------|-----------|------|---------|
| Equipment | ~0.5 m | ms–s | Gas flow, power |
| Plasma | ~cm | μs–ms | Species transport |
| Sheath | ~100 μm | ns–μs | Ion acceleration |
| Feature | ~10–100 nm | s–min | Profile evolution |
| Surface | ~nm | ps–ns | Adsorption, reaction |
7.2 Coupling Strategies
Hierarchical Approach
1. Solve equipment-scale flow → boundary conditions for plasma
2. Solve plasma model → fluxes to sheath
3. Solve sheath model → IEDF to surface
4. Solve feature-scale model → local etch rates
Embedded Multiscale
Feature-scale model embedded in equipment simulation:
- Sample representative features across wafer
- Compute local etch rates from local plasma conditions
- Interpolate for full wafer prediction
7.3 Reduced-Order Models
Plasma model simplification:
$$
n_e(P, W) = n_0 \cdot \left( \frac{P}{P_0} \right)^a \cdot \left( \frac{W}{W_0} \right)^b
$$
Where P is pressure, W is power.
Response surfaces:
$$
ER = \beta_0 + \sum_i \beta_i x_i + \sum_i \sum_j \beta_{ij} x_i x_j + \sum_i \beta_{ii} x_i^2
$$
8. Process Control Mathematics
8.1 Run-to-Run (R2R) Control
EWMA Controller
$$
u_k = u_{k-1} + K \cdot (y_{target} - y_{k-1})
$$
Where:
- $u_k$ — Recipe parameter at run $k$
- $y_k$ — Measured output at run $k$
- $K$ — Controller gain
Double EWMA (for drift)
$$
\hat{y}_{k+1} = \alpha y_k + (1-\alpha)\hat{y}_k
$$
$$
\hat{d}_{k+1} = \beta(\hat{y}_{k+1} - \hat{y}_k) + (1-\beta)\hat{d}_k
$$
$$
u_{k+1} = u_k - G(\hat{y}_{k+1} + \hat{d}_{k+1} - y_{target})
$$
8.2 Model Predictive Control (MPC)
Optimize over horizon N:
$$
\min_{u_k, ..., u_{k+N-1}} J = \sum_{i=1}^{N} \left[ \| y_{k+i} - y_{ref} \|_Q^2 + \| \Delta u_{k+i-1} \|_R^2 \right]
$$
Subject to:
- Process model: $y_{k+1} = f(y_k, u_k)$
- Input constraints: $u_{min} \leq u \leq u_{max}$
- Output constraints: $y_{min} \leq y \leq y_{max}$
- Rate constraints: $|\Delta u| \leq \Delta u_{max}$
8.3 Virtual Metrology
Predict wafer-level results from equipment data:
$$
\hat{y} = f(\vec{x}_{sensor})
$$
Where $\vec{x}_{sensor}$ includes:
- Optical emission spectroscopy (OES) signals
- RF impedance (voltage, current, phase)
- Pressure, flow rates
- Chamber wall temperature
- Endpoint detection signals
PLS (Partial Least Squares) Model
$$
\hat{y} = \vec{x}^T \cdot \vec{\beta}_{PLS}
$$
Neural Network Model
$$
\hat{y} = W_2 \cdot \sigma(W_1 \cdot \vec{x} + \vec{b}_1) + b_2
$$
8.4 Fault Detection and Classification (FDC)
Hotelling's T² Statistic
$$
T^2 = (\vec{x} - \vec{\mu})^T \Sigma^{-1} (\vec{x} - \vec{\mu})
$$
Alarm if $T^2 > T^2_{critical}(\alpha, p, n)$
Q-Statistic (SPE)
$$
Q = \|\vec{x} - \hat{\vec{x}}\|^2
$$
Where $\hat{\vec{x}}$ is PCA reconstruction.
8.5 Endpoint Detection
OES Endpoint
Monitor emission intensity ratio:
$$
R(t) = \frac{I_{\lambda_1}(t)}{I_{\lambda_2}(t)}
$$
Endpoint when:
$$
\left| \frac{dR}{dt} \right| > \text{threshold}
$$
9. Emerging Frontiers
9.1 Atomic Layer Etching (ALE)
Self-limiting process:
1. Modification step: Surface layer modified (e.g., chlorination)
2. Removal step: Modified layer removed by low-energy ions
$$
EPC = \Gamma_{sat} \cdot \delta_{modified}
$$
Where:
- $EPC$ — Etch per cycle (typically 0.5–2 Å)
- $\Gamma_{sat}$ — Saturation coverage
- $\delta_{modified}$ — Modified layer thickness
Synergy Parameter
$$
S = \frac{EPC_{ALE}}{EPC_{continuous}}
$$
High synergy indicates good self-limiting behavior.
9.2 Machine Learning Integration
Physics-Informed Neural Networks (PINNs)
Loss function includes physics constraints:
$$
\mathcal{L} = \mathcal{L}_{data} + \lambda \cdot \mathcal{L}_{physics}
$$
Where:
$$
\mathcal{L}_{physics} = \left\| \frac{\partial n}{\partial t} +
abla \cdot \vec{\Gamma} - S \right\|^2
$$
Gaussian Process Regression
For process optimization with uncertainty quantification:
$$
f(\vec{x}) \sim \mathcal{GP}(m(\vec{x}), k(\vec{x}, \vec{x}'))
$$
Posterior mean:
$$
\bar{f}(\vec{x}_) = \vec{k}_^T (K + \sigma_n^2 I)^{-1} \vec{y}
$$
9.3 Stochastic Effects at Nanoscale
Line Edge Roughness (LER)
At sub-10 nm features, discrete nature of reactions matters:
$$
\sigma_{LER}^2 = \frac{a^3}{L} \cdot \left( \frac{1}{\Gamma_{ion}} + \frac{1}{\Gamma_R \cdot s} \right)
$$
Where $a$ is atomic spacing, $L$ is line length.
Kinetic Monte Carlo (KMC)
Event selection probability:
$$
P_i = \frac{r_i}{\sum_j r_j}
$$
Time advance:
$$
\Delta t = -\frac{\ln(u)}{\sum_j r_j}
$$
Where $u \in (0,1)$ is uniform random.
Physical Constants
| Constant | Symbol | Value |
|----------|--------|-------|
| Boltzmann constant | $k_B$ | $1.38 \times 10^{-23}$ J K⁻¹ |
| Elementary charge | $e$ | $1.60 \times 10^{-19}$ C |
| Electron mass | $m_e$ | $9.11 \times 10^{-31}$ kg |
| Permittivity of free space | $\varepsilon_0$ | $8.85 \times 10^{-12}$ F m⁻¹ |
| Avogadro's number | $N_A$ | $6.02 \times 10^{23}$ mol⁻¹ |
| Stefan-Boltzmann constant | $\sigma$ | $5.67 \times 10^{-8}$ W m⁻² K⁻⁴ |
Typical Process Parameters
| Parameter | Typical Range | Units |
|-----------|---------------|-------|
| Pressure | 1–100 | mTorr |
| RF Power | 100–2000 | W |
| Bias Voltage | 50–500 | V |
| Electron Temperature | 2–5 | eV |
| Electron Density | 10⁹–10¹² | cm⁻³ |
| Ion Energy | 50–500 | eV |
| Etch Rate | 50–500 | nm min⁻¹ |