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513 technical terms and definitions

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Showing page 5 of 11 (513 entries)

albert,foundation model

Lighter BERT using parameter sharing and factorization.

albumentations,fast,image

Albumentations is fast image augmentation library. Many transforms.

ald (atomic layer deposition),ald,atomic layer deposition,cvd

Sequential self-limiting surface reactions for atomic-level thickness control.

ald cycle,cvd

One precursor pulse + purge + reactant pulse + purge.

aleatoric uncertainty, ai safety

Aleatoric uncertainty comes from inherent randomness irreducible even with perfect knowledge.

aleatoric uncertainty,ai safety

Inherent randomness in data.

alert configuration,monitoring

Set thresholds and notifications for issues.

alerting,pagerduty,oncall

Alerts notify on-call when issues occur. PagerDuty, OpsGenie. Escalation policies.

alias structure,doe

Which effects are confounded.

alias-free gan, multimodal ai

Alias-free GANs eliminate coordinate-dependent artifacts through continuous signal processing.

alibi (attention with linear biases),alibi,attention with linear biases,transformer

Simple relative position encoding.

aligner, manufacturing operations

Aligners position wafers accurately using notch or flat detection.

aligner,automation

Mechanism to orient wafer notch or flat to a standard position.

alignment marks,lithography

Reference patterns on wafer used to align each layer.

alignment tax,capability tradeoff,tradeoff

Alignment tax: safety measures may reduce capability. Balance helpfulness and harmlessness.

alignment,rlhf,dpo,preferences

Alignment = making models follow human values and instructions. RLHF/DPO leverage human preference data to push the model toward desired behavior.

all-mlp architectures, computer vision

Vision models without convolution or attention.

all-reduce operation, distributed training

Efficiently aggregate across nodes.

all-to-all communication, distributed training

Exchange data between all devices.

allegro, chemistry ai

Fast equivariant neural network.

allegro, graph neural networks

Allegro achieves fast equivariant message passing through strict locality and efficient tensor operations.

allocation,industry

Distribute limited supply among customers.

alloy design, materials science

Optimize metallic alloy compositions.

alloy scattering, device physics

Scattering in mixed semiconductors.

alpaca, training techniques

Alpaca demonstrates instruction-following through distillation from stronger models.

alpaca,stanford,instruction

Alpaca is Stanford instruction-tuned Llama. Self-instruct method.

alpacaeval,evaluation

Automated evaluation using LLM-as-judge.

alpha testing, quality

Internal testing.

alphacode,code ai

DeepMind's competitive programming model.

alphafold,healthcare ai

DeepMind's protein structure prediction system.

alphafold,protein structure,deepmind

AlphaFold predicts protein 3D structure from sequence. Revolutionary for biology. Nobel Prize work.

als implicit, als, recommendation systems

Alternating Least Squares with implicit feedback handles binary observations like clicks without explicit ratings.

als, als, recommendation systems

Alternating Least Squares optimizes matrix factorization by alternately fixing user factors and item factors for efficient large-scale training.

altair,declarative,visualization

Altair is declarative visualization. Vega-Lite based.

alternating psm (altpsm),alternating psm,altpsm,lithography

Adjacent features have opposite phase.

alternative chemistries, environmental & sustainability

Alternative chemistries develop less hazardous process chemicals maintaining performance while reducing environmental impact.

aluminum etch,al metal etch,aluminum metal etch modeling,al etch modeling,aluminum chlorine etch,alcl3,metal etch plasma,aluminum plasma etch,bcl3 etch

# Aluminum Metal Etch Mathematical Modeling 1. Overview 1.1 Why Aluminum Etch Modeling is Complex Aluminum etching (typically using $\text{Cl}_2/\text{BCl}_3$ plasmas) involves multiple coupled physical and chemical phenomena: - Plasma generation and transport → determines species fluxes to wafer - Ion-surface interactions → physical and chemical mechanisms - Surface reactions → Langmuir-Hinshelwood kinetics - Feature-scale evolution → profile development inside trenches/vias - Redeposition and passivation → sidewall chemistry 1.2 Fundamental Reaction The basic aluminum chlorination reaction: $$ \text{Al} + 3\text{Cl} \rightarrow \text{AlCl}_3 \uparrow $$ Complications requiring sophisticated modeling: - Breaking through native $\text{Al}_2\text{O}_3$ layer (15-30 Å) - Maintaining profile anisotropy - Controlling selectivity to mask and underlayers - Managing Cu residues in Al-Cu alloys 2. Kinetic and Chemical Rate Modeling 2.1 General Etch Rate Formulation A comprehensive etch rate model combines three primary mechanisms: $$ ER = \underbrace{k_{th} \cdot \Gamma_{Cl} \cdot f(\theta)}_{\text{thermal chemical}} + \underbrace{Y_s \cdot \Gamma_{ion} \cdot \sqrt{E_{ion}}}_{\text{physical sputtering}} + \underbrace{\beta \cdot \Gamma_{ion}^a \cdot \Gamma_{Cl}^b \cdot E_{ion}^c}_{\text{ion-enhanced (synergistic)}} $$ Parameter Definitions: | Symbol | Description | Units | |--------|-------------|-------| | $\Gamma_{Cl}$ | Neutral chlorine flux | $\text{cm}^{-2}\text{s}^{-1}$ | | $\Gamma_{ion}$ | Ion flux | $\text{cm}^{-2}\text{s}^{-1}$ | | $E_{ion}$ | Ion energy | eV | | $\theta$ | Surface coverage of reactive species | dimensionless | | $Y_s$ | Physical sputtering yield | atoms/ion | | $\beta$ | Synergy coefficient | varies | | $a, b, c$ | Exponents (typically 0.5-1) | dimensionless | 2.2 Surface Coverage Dynamics The reactive site balance follows Langmuir-Hinshelwood kinetics: $$ \frac{d\theta}{dt} = k_{ads} \cdot \Gamma_{Cl} \cdot (1-\theta) - k_{des} \cdot \theta \cdot \exp\left(-\frac{E_d}{k_B T}\right) - Y_{react}(\theta, E_{ion}) \cdot \Gamma_{ion} \cdot \theta $$ Term-by-term breakdown: - Term 1: $k_{ads} \cdot \Gamma_{Cl} \cdot (1-\theta)$ — Adsorption rate (proportional to empty sites) - Term 2: $k_{des} \cdot \theta \cdot \exp(-E_d/k_B T)$ — Thermal desorption (Arrhenius) - Term 3: $Y_{react} \cdot \Gamma_{ion} \cdot \theta$ — Ion-induced reaction/removal Steady-State Solution ($d\theta/dt = 0$): $$ \theta_{ss} = \frac{k_{ads} \cdot \Gamma_{Cl}}{k_{ads} \cdot \Gamma_{Cl} + k_{des} \cdot e^{-E_d/k_B T} + Y_{react} \cdot \Gamma_{ion}} $$ 2.3 Temperature Dependence All rate constants follow Arrhenius behavior: $$ k_i(T) = A_i \cdot \exp\left(-\frac{E_{a,i}}{k_B T}\right) $$ Typical activation energies for aluminum etching: - Ion-enhanced reactions: $E_a \approx 0.1 - 0.3 \text{ eV}$ - Purely thermal processes: $E_a \approx 0.5 - 1.0 \text{ eV}$ - Chlorine desorption: $E_d \approx 0.3 - 0.5 \text{ eV}$ 2.4 Complete Etch Rate Expression Combining all terms with explicit dependencies: $$ ER(T, \Gamma_{ion}, \Gamma_{Cl}, E_{ion}) = A_1 e^{-E_1/k_B T} \Gamma_{Cl} \theta + Y_0 \Gamma_{ion} \sqrt{E_{ion}} + A_2 e^{-E_2/k_B T} \Gamma_{ion}^{0.5} \Gamma_{Cl}^{0.5} E_{ion}^{0.5} $$ 3. Ion-Surface Interaction Physics 3.1 Ion Energy Distribution Function (IEDF) For RF-biased electrodes, the IEDF is approximately bimodal: $$ f(E) \propto \frac{1}{\sqrt{|E - E_{dc}|}} \quad \text{for } E_{dc} - E_{rf} < E < E_{dc} + E_{rf} $$ Key parameters: - $E_{dc} = e \cdot V_{dc}$ — DC self-bias energy - $E_{rf} = e \cdot V_{rf}$ — RF amplitude energy - Peak separation: $\Delta E = 2 E_{rf}$ Collisional effects: In collisional sheaths, charge-exchange collisions broaden the distribution: $$ f(E) \propto \exp\left(-\frac{E}{\bar{E}}\right) \cdot \left[1 + \text{erf}\left(\frac{E - E_{dc}}{\sigma_E}\right)\right] $$ 3.2 Ion Angular Distribution Function (IADF) The angular spread is approximately Gaussian: $$ f(\theta) = \frac{1}{\sqrt{2\pi}\sigma_\theta} \exp\left(-\frac{\theta^2}{2\sigma_\theta^2}\right) $$ Angular spread calculation: $$ \sigma_\theta \approx \sqrt{\frac{k_B T_i}{e V_{sheath}}} \approx \arctan\left(\sqrt{\frac{T_i}{V_{sheath}}}\right) $$ Typical values: - Ion temperature: $T_i \approx 0.05 - 0.5 \text{ eV}$ - Sheath voltage: $V_{sheath} \approx 50 - 500 \text{ V}$ - Angular spread: $\sigma_\theta \approx 2° - 5°$ 3.3 Physical Sputtering Yield Yamamura Formula (Angular Dependence) $$ Y(\theta) = Y(0°) \cdot \cos^{-f}(\theta) \cdot \exp\left[b\left(1 - \frac{1}{\cos\theta}\right)\right] $$ Parameters for aluminum: - $f \approx 1.5 - 2.0$ - $b \approx 0.1 - 0.3$ (depends on ion/target mass ratio) - Maximum yield typically at $\theta \approx 60° - 70°$ Sigmund Theory (Energy Dependence) $$ Y(E) = \frac{0.042 \cdot Q \cdot \alpha(M_2/M_1) \cdot S_n(E)}{U_s} $$ Where: - $S_n(E)$ = nuclear stopping power (Thomas-Fermi) - $U_s = 3.4 \text{ eV}$ (surface binding energy for Al) - $Q$ = dimensionless factor ($\approx 1$ for metals) - $\alpha$ = mass-dependent parameter - $M_1, M_2$ = projectile and target masses Nuclear Stopping Power $$ S_n(\epsilon) = \frac{0.5 \ln(1 + 1.2288\epsilon)}{\epsilon + 0.1728\sqrt{\epsilon} + 0.008\epsilon^{0.1504}} $$ With reduced energy: $$ \epsilon = \frac{M_2 E}{(M_1 + M_2) Z_1 Z_2 e^2} \cdot \frac{a_{TF}}{1} $$ 3.4 Ion-Enhanced Etching Yield The total etch yield combines mechanisms: $$ Y_{total} = Y_{physical} + Y_{chemical} + Y_{synergistic} $$ Synergistic enhancement factor: $$ \eta = \frac{Y_{total}}{Y_{physical} + Y_{chemical}} > 1 $$ For Al/Cl₂ systems, $\eta$ can exceed 10 under optimal conditions. 4. Plasma Modeling (Reactor Scale) 4.1 Species Continuity Equations For each species $i$ (electrons, ions, neutrals): $$ \frac{\partial n_i}{\partial t} + \nabla \cdot \vec{\Gamma}_i = S_i - L_i $$ Flux expressions: - Drift-diffusion: $\vec{\Gamma}_i = -D_i \nabla n_i + \mu_i n_i \vec{E}$ - Full momentum: $\vec{\Gamma}_i = n_i \vec{v}_i$ with momentum equation Source/sink terms: $$ S_i = \sum_j k_{ij} n_j n_e \quad \text{(ionization, dissociation)} $$ $$ L_i = \sum_j k_{ij}^{loss} n_i n_j \quad \text{(recombination, attachment)} $$ 4.2 Electron Energy Balance $$ \frac{\partial}{\partial t}\left(\frac{3}{2} n_e k_B T_e\right) + \nabla \cdot \vec{Q}_e = P_{abs} - P_{loss} $$ Heat flux: $$ \vec{Q}_e = \frac{5}{2} k_B T_e \vec{\Gamma}_e - \kappa_e \nabla T_e $$ Power absorption (ICP): $$ P_{abs} = \frac{1}{2} \text{Re}(\sigma_p) |E|^2 $$ Collisional losses: $$ P_{loss} = \sum_j n_e n_j k_j \varepsilon_j $$ Where $\varepsilon_j$ is the energy loss per collision event $j$. 4.3 Plasma Conductivity $$ \sigma_p = \frac{n_e e^2}{m_e(\nu_m + i\omega)} $$ Skin depth: $$ \delta = \sqrt{\frac{2}{\omega \mu_0 \text{Re}(\sigma_p)}} $$ 4.4 Electromagnetic Field Equations Maxwell's equations (frequency domain): $$ \nabla \times \vec{E} = -i\omega \vec{B} $$ $$ \nabla \times \vec{B} = \mu_0 \sigma_p \vec{E} + i\omega \mu_0 \epsilon_0 \vec{E} $$ Wave equation: $$ \nabla^2 \vec{E} + \left(\frac{\omega^2}{c^2} - i\omega\mu_0\sigma_p\right)\vec{E} = 0 $$ 4.5 Sheath Physics Child-Langmuir Law (Collisionless Sheath) $$ J_{ion} = \frac{4\epsilon_0}{9}\sqrt{\frac{2e}{M}} \cdot \frac{V_s^{3/2}}{s^2} $$ Where: - $J_{ion}$ = ion current density - $V_s$ = sheath voltage - $s$ = sheath thickness - $M$ = ion mass Bohm Criterion Ions must enter sheath with velocity: $$ v_{Bohm} = \sqrt{\frac{k_B T_e}{M}} $$ Ion flux at sheath edge: $$ \Gamma_{ion} = n_s \cdot v_{Bohm} = 0.61 \cdot n_0 \sqrt{\frac{k_B T_e}{M}} $$ Sheath Thickness $$ s \approx \lambda_D \cdot \left(\frac{2 e V_s}{k_B T_e}\right)^{3/4} $$ Debye length: $$ \lambda_D = \sqrt{\frac{\epsilon_0 k_B T_e}{n_e e^2}} $$ 5. Feature-Scale Profile Evolution 5.1 Level Set Method The surface is represented implicitly by $\phi(\vec{r}, t) = 0$: $$ \frac{\partial \phi}{\partial t} + V_n |\nabla \phi| = 0 $$ Normal velocity calculation: $$ V_n(\vec{r}) = \int_0^{E_{max}} \int_0^{\theta_{max}} Y(E, \theta_{local}) \cdot f_{IEDF}(E) \cdot f_{IADF}(\theta) \cdot \Gamma_{ion}(\vec{r}) \, dE \, d\theta $$ Plus contributions from: - Neutral chemical etching - Redeposition - Surface diffusion 5.2 Hamilton-Jacobi Formulation $$ \frac{\partial \phi}{\partial t} + H(\nabla \phi, \vec{r}, t) = 0 $$ Hamiltonian for etch: $$ H = V_n \sqrt{\phi_x^2 + \phi_y^2 + \phi_z^2} $$ With $V_n$ dependent on: - Local surface normal: $\hat{n} = -\nabla\phi / |\nabla\phi|$ - Local fluxes: $\Gamma(\vec{r})$ - Local angles: $\theta = \arccos(\hat{n} \cdot \hat{z})$ 5.3 Visibility and View Factors Direct Flux The flux reaching a point inside a feature depends on solid angle visibility: $$ \Gamma_{direct}(\vec{r}) = \int_{\Omega_{visible}} \Gamma_0 \cdot \cos\theta \cdot \frac{d\Omega}{\pi} $$ Reflected/Reemitted Flux For neutrals with sticking coefficient $s$: $$ \Gamma_{total}(\vec{r}) = \Gamma_{direct}(\vec{r}) + (1-s) \cdot \Gamma_{reflected}(\vec{r}) $$ This leads to coupled integral equations: $$ \Gamma(\vec{r}) = \Gamma_{plasma}(\vec{r}) + (1-s) \int_{S'} K(\vec{r}, \vec{r'}) \Gamma(\vec{r'}) dS' $$ Kernel function: $$ K(\vec{r}, \vec{r'}) = \frac{\cos\theta \cos\theta'}{\pi |\vec{r} - \vec{r'}|^2} \cdot V(\vec{r}, \vec{r'}) $$ Where $V(\vec{r}, \vec{r'})$ is the visibility function (1 if visible, 0 otherwise). 5.4 Aspect Ratio Dependent Etching (ARDE) Empirical model: $$ \frac{ER(AR)}{ER_0} = \frac{1}{1 + (AR/AR_c)^n} $$ Where: - $AR = \text{depth}/\text{width}$ (aspect ratio) - $AR_c$ = critical aspect ratio (process-dependent) - $n \approx 1 - 2$ Knudsen transport model: $$ \Gamma_{neutral}(z) = \Gamma_0 \cdot \frac{W}{W + \alpha \cdot z} $$ Where: - $z$ = feature depth - $W$ = feature width - $\alpha$ = Clausing factor (depends on geometry and sticking) Clausing factor for cylinder: $$ \alpha = \frac{8}{3} \cdot \frac{1 - s}{s} $$ 6. Aluminum-Specific Phenomena 6.1 Native Oxide Breakthrough $\text{Al}_2\text{O}_3$ (15-30 Å native oxide) requires physical sputtering: $$ ER_{oxide} \approx Y_{\text{BCl}_3^+}(E) \cdot \Gamma_{ion} $$ Why BCl₃ is critical: 1. Heavy $\text{BCl}_3^+$ ions provide efficient momentum transfer 2. BCl₃ scavenges oxygen chemically: $$ 2\text{BCl}_3 + \text{Al}_2\text{O}_3 \rightarrow 2\text{AlCl}_3 \uparrow + \text{B}_2\text{O}_3 $$ Breakthrough time: $$ t_{breakthrough} = \frac{d_{oxide}}{ER_{oxide}} = \frac{d_{oxide}}{Y_{BCl_3^+} \cdot \Gamma_{ion}} $$ 6.2 Sidewall Passivation Dynamics Anisotropic profiles require passivation of sidewalls: $$ \frac{d\tau_{pass}}{dt} = R_{dep}(\Gamma_{redeposition}, s_{stick}) - R_{removal}(\Gamma_{ion}, \theta_{sidewall}) $$ Deposition sources: - $\text{AlCl}_x$ redeposition from etch products - Photoresist erosion products (C, H, O, N) - Intentional additives: $\text{N}_2 \rightarrow \text{AlN}$ formation Why sidewalls are protected: At grazing incidence ($\theta \approx 85° - 90°$): - Ion flux geometric factor: $\Gamma_{sidewall} = \Gamma_0 \cdot \cos(90° - \alpha) \approx \Gamma_0 \cdot \sin\alpha$ - For $\alpha = 5°$: $\Gamma_{sidewall} \approx 0.09 \cdot \Gamma_0$ - Sputtering yield at grazing incidence approaches zero - Net passivation accumulates → blocks lateral etching 6.3 Notching and Charging Effects At dielectric interfaces, differential charging causes ion deflection: Surface charge evolution: $$ \frac{d\sigma}{dt} = J_{ion} - J_{electron} $$ Where: - $\sigma$ = surface charge density (C/cm²) - $J_{ion}$ = ion current (always positive) - $J_{electron}$ = electron current (depends on local potential) Local electric field: $$ \vec{E}_{charging} = -\nabla V_{charging} $$ Laplace equation in feature: $$ \nabla^2 V = -\frac{\rho}{\epsilon_0} \quad \text{(with } \rho = 0 \text{ in vacuum)} $$ Modified ion trajectory: $$ m \frac{d^2\vec{r}}{dt^2} = e\left(\vec{E}_{sheath} + \vec{E}_{charging}\right) $$ Result: Ions deflect toward charged surfaces → notching at feature bottom. Mitigation strategies: - Pulsed plasmas (allow electron neutralization) - Low-frequency bias (time for charge equilibration) - Conductive underlayers 6.4 Copper Residue Formation (Al-Cu Alloys) Al-Cu alloys (0.5-4% Cu) leave Cu residues because Cu chlorides are less volatile: Volatility comparison: | Species | Sublimation/Boiling Point | |---------|---------------------------| | $\text{AlCl}_3$ | 180°C (sublimes) | | $\text{CuCl}$ | 430°C (sublimes) | | $\text{CuCl}_2$ | 300°C (decomposes) | Residue accumulation rate: $$ \frac{d[\text{Cu}]_{surface}}{dt} = x_{Cu} \cdot ER_{Al} - ER_{Cu} $$ Where: - $x_{Cu}$ = Cu atomic fraction in alloy - At low temperature: $ER_{Cu} \ll x_{Cu} \cdot ER_{Al}$ Solutions: - Elevated substrate temperature ($>$150°C) - Increased BCl₃ fraction - Post-etch treatments 7. Numerical Methods 7.1 Level Set Discretization Upwind Finite Differences Using Hamilton-Jacobi ENO (Essentially Non-Oscillatory) schemes: $$ \phi_i^{n+1} = \phi_i^n - \Delta t \cdot H(\phi_x^-, \phi_x^+, \phi_y^-, \phi_y^+) $$ One-sided derivatives: $$ \phi_x^- = \frac{\phi_i - \phi_{i-1}}{\Delta x}, \quad \phi_x^+ = \frac{\phi_{i+1} - \phi_i}{\Delta x} $$ Godunov flux for $H = V_n |\nabla\phi|$: $$ H^{Godunov} = \begin{cases} V_n \sqrt{\max(\phi_x^{-,+},0)^2 + \max(\phi_y^{-,+},0)^2} & \text{if } V_n > 0 \\ V_n \sqrt{\max(\phi_x^{+,-},0)^2 + \max(\phi_y^{+,-},0)^2} & \text{if } V_n < 0 \end{cases} $$ Reinitialization Maintain $|\nabla\phi| = 1$ using: $$ \frac{\partial \phi}{\partial \tau} = \text{sign}(\phi_0)(1 - |\nabla\phi|) $$ Iterate in pseudo-time $\tau$ until convergence. 7.2 Monte Carlo Feature-Scale Simulation Algorithm: 1. INITIALIZE surface mesh 2. FOR each time step: a. FOR i = 1 to N_particles: - Sample particle from IEDF, IADF - Launch from plasma boundary - TRACE trajectory until surface hit - APPLY reaction probability: * Etch (remove cell) with probability P_etch * Reflect with probability P_reflect * Deposit with probability P_deposit b. UPDATE surface mesh c. CHECK for convergence 3. OUTPUT final profile Variance reduction techniques: - Importance sampling: Weight particles toward features of interest - Particle splitting: Increase statistics in critical regions - Russian roulette: Terminate low-weight particles probabilistically 7.3 Coupled Multi-Scale Modeling | Scale | Domain | Method | Outputs | |-------|--------|--------|---------| | Reactor | m | Fluid/hybrid plasma | $n_e$, $T_e$, species densities | | Sheath | mm | PIC or fluid | IEDF, IADF, fluxes | | Feature | nm-μm | Level set / Monte Carlo | Profile evolution | | Atomistic | Å | MD / DFT | Yields, sticking coefficients | Coupling strategy: $$ \text{Reactor} \xrightarrow{\Gamma_i, f(E), f(\theta)} \text{Feature} \xrightarrow{ER(\vec{r})} \text{Reactor} $$ 7.4 Plasma Solver Discretization Finite element for Poisson's equation: $$ \nabla \cdot (\epsilon \nabla V) = -\rho $$ Weak form: $$ \int_\Omega \epsilon \nabla V \cdot \nabla w \, d\Omega = \int_\Omega \rho \, w \, d\Omega $$ Finite volume for transport: $$ \frac{d(n_i V_j)}{dt} = -\sum_{faces} \Gamma_i \cdot \hat{n} \cdot A + S_i V_j $$ 8. Process Window and Optimization 8.1 Response Surface Modeling Quadratic response surface: $$ ER = \beta_0 + \sum_{i=1}^{k} \beta_i x_i + \sum_{i=1}^{k} \beta_{ii} x_i^2 + \sum_{i T_i \end{cases} $$ Optimization problem: $$ \max_{\vec{x}} D(\vec{x}) $$ Subject to: - $85° < \text{sidewall angle} < 90°$ - $\text{Selectivity}_{Al:resist} > 3:1$ - $\text{Selectivity}_{Al:TiN} > 10:1$ - $\text{Uniformity} < 3\%$ (1σ) 8.3 Virtual Metrology Prediction model: $$ \vec{y}_{etch} = f_{ML}\left(\vec{x}_{recipe}, \vec{x}_{OES}, \vec{x}_{chamber}\right) $$ Input features: - Recipe: Power, pressure, flows, time - OES: Emission line intensities (e.g., Al 396nm, Cl 837nm) - Chamber: Impedance, temperature, previous wafer history Machine learning approaches: - Neural networks (for complex nonlinear relationships) - Gaussian processes (with uncertainty quantification) - Partial least squares (for high-dimensional, correlated inputs) 8.4 Run-to-Run Control EWMA (Exponentially Weighted Moving Average) controller: $$ \vec{x}_{k+1} = \vec{x}_k + \Lambda G^{-1}(\vec{y}_{target} - \vec{y}_k) $$ Where: - $\Lambda$ = diagonal weighting matrix (0 < λ < 1) - $G$ = process gain matrix ($\partial y / \partial x$) Drift compensation: $$ \vec{x}_{k+1} = \vec{x}_k + \Lambda_1 G^{-1}(\vec{y}_{target} - \vec{y}_k) + \Lambda_2 (\vec{x}_{k} - \vec{x}_{k-1}) $$ 9. Equations: | Physics | Governing Equation | |---------|-------------------| | Etch rate | $ER = k\Gamma_{Cl}\theta + Y\Gamma_{ion}\sqrt{E} + \beta\Gamma_{ion}\Gamma_{Cl}E^c$ | | Surface coverage | $\theta = \dfrac{k_{ads}\Gamma}{k_{ads}\Gamma + k_{des}e^{-E_d/kT} + Y\Gamma_{ion}}$ | | Profile evolution | $\dfrac{\partial\phi}{\partial t} + V_n|\nabla\phi| = 0$ | | Ion flux (sheath) | $J_{ion} = \dfrac{4\epsilon_0}{9}\sqrt{\dfrac{2e}{M}} \cdot \dfrac{V^{3/2}}{s^2}$ | | ARDE | $\dfrac{ER(AR)}{ER_0} = \dfrac{1}{1 + (AR/AR_c)^n}$ | | View factor | $\Gamma(\vec{r}) = \displaystyle\int_{\Omega} \Gamma_0 \cos\theta \, \dfrac{d\Omega}{\pi}$ | | Sputtering yield | $Y(\theta) = Y_0 \cos^{-f}\theta \cdot \exp\left[b\left(1 - \dfrac{1}{\cos\theta}\right)\right]$ | | Species transport | $\dfrac{\partial n_i}{\partial t} + \nabla \cdot \vec{\Gamma}_i = S_i - L_i$ | 10. Modern Developments 10.1 Machine Learning Integration Applications: - Yield prediction: Neural networks trained on MD simulation data - Surrogate models: Replace expensive PDE solvers for real-time optimization - Process control: Reinforcement learning for adaptive recipes Example: Gaussian Process for Etch Rate: $$ ER(\vec{x}) \sim \mathcal{GP}\left(m(\vec{x}), k(\vec{x}, \vec{x}')\right) $$ With squared exponential kernel: $$ k(\vec{x}, \vec{x}') = \sigma_f^2 \exp\left(-\frac{|\vec{x} - \vec{x}'|^2}{2\ell^2}\right) $$ 10.2 Atomistic-Continuum Bridging ReaxFF molecular dynamics: - Reactive force fields for Al-Cl-O systems - Calculate fundamental yields and sticking coefficients - Feed into continuum models DFT calculations: - Adsorption energies: $E_{ads} = E_{surface+adsorbate} - E_{surface} - E_{adsorbate}$ - Activation barriers via NEB (Nudged Elastic Band) - Electronic structure effects on reactivity 10.3 Digital Twins Components: - Real-time sensor data ingestion - Physics-based + ML hybrid models - Predictive maintenance algorithms - Virtual process development Update equation: $$ \vec{\theta}_{model}^{(k+1)} = \vec{\theta}_{model}^{(k)} + K_k \left(\vec{y}_{measured} - \vec{y}_{predicted}\right) $$ 10.4 Uncertainty Quantification Bayesian calibration: $$ p(\vec{\theta}|\vec{y}) \propto p(\vec{y}|\vec{\theta}) \cdot p(\vec{\theta}) $$ Propagation through models: $$ \text{Var}(y) \approx \sum_i \left(\frac{\partial y}{\partial \theta_i}\right)^2 \text{Var}(\theta_i) $$ Monte Carlo uncertainty: $$ \bar{y} \pm t_{\alpha/2} \cdot \frac{s}{\sqrt{N}} $$ Physical Constants | Constant | Symbol | Value | |----------|--------|-------| | Boltzmann constant | $k_B$ | $1.381 \times 10^{-23}$ J/K | | Electron charge | $e$ | $1.602 \times 10^{-19}$ C | | Electron mass | $m_e$ | $9.109 \times 10^{-31}$ kg | | Permittivity of vacuum | $\epsilon_0$ | $8.854 \times 10^{-12}$ F/m | | Al atomic mass | $M_{Al}$ | 26.98 amu | | Al surface binding energy | $U_s$ | 3.4 eV | Process Conditions | Parameter | Typical Range | |-----------|---------------| | Pressure | 5-50 mTorr | | Source power (ICP) | 200-1000 W | | Bias power (RF) | 50-300 W | | Cl₂ flow | 20-100 sccm | | BCl₃ flow | 20-80 sccm | | Temperature | 20-80°C | | Etch rate | 300-800 nm/min |

aluminum wire bonding, packaging

Use aluminum wire.

always-on domain,design

Power domain that stays on.

amazon q,aws,assistant

Amazon Q is AWS AI assistant. Coding, AWS expertise.

ambient intelligence,emerging tech

AI seamlessly integrated into environment.

ambipolar diffusion, device physics

Coupled electron-hole diffusion.

amc monitor, amc, manufacturing operations

Airborne Molecular Contamination monitors detect trace organic and acid vapors.

amhs, amhs, facility

Integrated material transport.

amoebanet, neural architecture search

AmoebaNet uses regularized evolution for neural architecture search demonstrating that simple aging-based regularization improves discovered architectures.

amorphization,implant

Heavy implant turns crystalline silicon amorphous.

amorphous silicon,cvd

Disordered silicon deposited at lower temperatures.

amsaa model, amsaa, business & standards

AMSAA model provides statistical framework for reliability growth analysis.

amsaa model, amsaa, reliability

Army reliability growth model.

analogical prompting, prompting techniques

Analogical prompting uses related examples from different domains to guide reasoning.