← Back to AI Factory Chat

AI Factory Glossary

9,967 technical terms and definitions

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Showing page 25 of 200 (9,967 entries)

cleanroom,clean room,fab environment,class 1,iso 14644,particle contamination,hepa,ulpa,laminar flow,bunny suit,gowning procedure,mathematical modeling

# Semiconductor Manufacturing Cleanroom: Mathematical Modeling ## 1. Cleanroom Environment Modeling ### 1.1 Particle Dynamics The particle concentration in a cleanroom follows the **continuity equation**: $$ \frac{\partial C}{\partial t} + \nabla \cdot (C\vec{v}) = S - \lambda C $$ **Variable Definitions:** - $C$ — Particle concentration (particles/m³) - $\vec{v}$ — Air velocity vector (m/s) - $S$ — Source term / generation rate (particles/m³·s) - $\lambda$ — Removal rate coefficient (1/s) - $t$ — Time (s) **Particle Settling Velocity (Stokes' Law):** $$ v_s = \frac{\rho_p d_p^2 g C_c}{18 \mu} $$ - $\rho_p$ — Particle density (kg/m³) - $d_p$ — Particle diameter (m) - $g$ — Gravitational acceleration (9.81 m/s²) - $C_c$ — Cunningham slip correction factor - $\mu$ — Dynamic viscosity of air (Pa·s) **Cunningham Slip Correction Factor:** $$ C_c = 1 + \frac{\lambda_m}{d_p}\left[2.34 + 1.05 \exp\left(-0.39 \frac{d_p}{\lambda_m}\right)\right] $$ - $\lambda_m$ — Mean free path of air molecules (~65 nm at STP) ### 1.2 Airflow Modeling Cleanroom airflow is governed by the **Navier-Stokes equations**: $$ \rho\left(\frac{\partial \vec{v}}{\partial t} + \vec{v} \cdot \nabla\vec{v}\right) = -\nabla p + \mu \nabla^2 \vec{v} + \vec{f} $$ **Variable Definitions:** - $\rho$ — Air density (kg/m³) - $\vec{v}$ — Velocity vector (m/s) - $p$ — Pressure (Pa) - $\mu$ — Dynamic viscosity (Pa·s) - $\vec{f}$ — Body forces (N/m³) **Continuity Equation (Incompressible Flow):** $$ \nabla \cdot \vec{v} = 0 $$ **Reynolds Number (Flow Regime Characterization):** $$ Re = \frac{\rho v L}{\mu} $$ - $L$ — Characteristic length (m) - $Re < 2300$ — Laminar flow (desired in cleanrooms) - $Re > 4000$ — Turbulent flow ### 1.3 Filtration Efficiency **Overall Filter Penetration:** $$ P = P_{\text{diffusion}} + P_{\text{interception}} + P_{\text{impaction}} $$ **Diffusion Mechanism (Small Particles < 0.1 µm):** $$ \eta_D = 2.7 \cdot Pe^{-2/3} $$ - $Pe = \frac{v \cdot d_f}{D}$ — Péclet number - $D = \frac{k_B T C_c}{3 \pi \mu d_p}$ — Particle diffusion coefficient - $d_f$ — Filter fiber diameter **Interception Mechanism:** $$ \eta_R = 0.6 \cdot \frac{\alpha}{Ku} \cdot \left(\frac{d_p}{d_f}\right)^2 $$ - $\alpha$ — Fiber volume fraction (solidity) - $Ku$ — Kuwabara hydrodynamic factor **HEPA/ULPA Efficiency Classification:** | Class | Efficiency | MPPS Range | |:------|:-----------|:-----------| | HEPA H13 | ≥ 99.95% | 0.1–0.3 µm | | HEPA H14 | ≥ 99.995% | 0.1–0.3 µm | | ULPA U15 | ≥ 99.9995% | 0.1–0.2 µm | | ULPA U16 | ≥ 99.99995% | 0.1–0.2 µm | ### 1.4 Temperature and Humidity Control **Heat Transfer Equation:** $$ \rho c_p \frac{\partial T}{\partial t} = k \nabla^2 T + \dot{q} $$ - $c_p$ — Specific heat capacity (J/kg·K) - $k$ — Thermal conductivity (W/m·K) - $\dot{q}$ — Volumetric heat generation (W/m³) **Psychrometric Relations (Humidity):** $$ \omega = 0.622 \cdot \frac{p_v}{p_{atm} - p_v} $$ - $\omega$ — Humidity ratio (kg water/kg dry air) - $p_v$ — Partial pressure of water vapor (Pa) - $p_{atm}$ — Atmospheric pressure (Pa) **Relative Humidity:** $$ RH = \frac{p_v}{p_{sat}(T)} \times 100\% $$ - $p_{sat}(T)$ — Saturation vapor pressure at temperature $T$ ## 2. Process Equipment Mathematics ### 2.1 Lithography #### 2.1.1 Aerial Image Formation **Hopkins Equation (Partially Coherent Imaging):** $$ I(x,y) = \left|\iint TCC(f_1, f_2; f_1', f_2') \cdot M(f_1, f_2) \cdot M^*(f_1', f_2') \, df_1 \, df_2 \, df_1' \, df_2'\right| $$ - $I(x,y)$ — Aerial image intensity - $TCC$ — Transmission Cross Coefficient - $M$ — Mask transmission function (Fourier domain) - $M^*$ — Complex conjugate of mask function #### 2.1.2 Resolution Limits **Rayleigh Criterion:** $$ R = k_1 \cdot \frac{\lambda}{NA} $$ - $R$ — Minimum resolvable feature (m) - $k_1$ — Process factor (0.25 – 0.8) - $\lambda$ — Exposure wavelength (m) - $NA$ — Numerical aperture **Depth of Focus:** $$ DOF = k_2 \cdot \frac{\lambda}{NA^2} $$ - $k_2$ — Process factor (~0.5 – 1.0) #### 2.1.3 Exposure Dose **Mack Model (Resist Response):** $$ E_{eff} = E_0 \cdot \exp\left(-\alpha z\right) \cdot \left[1 + r \cdot \exp\left(-2\alpha(D-z)\right)\right] $$ - $E_0$ — Incident dose (mJ/cm²) - $\alpha$ — Absorption coefficient (1/µm) - $z$ — Depth in resist - $r$ — Substrate reflectivity - $D$ — Resist thickness **Critical Dimension (CD) Sensitivity:** $$ \frac{\Delta CD}{CD} = \frac{1}{\gamma} \cdot \frac{\Delta E}{E} $$ - $\gamma$ — Resist contrast ### 2.2 Chemical Vapor Deposition (CVD) #### 2.2.1 Film Growth Rate **Surface Reaction Limited:** $$ R = k_s \cdot C_s $$ **Mass Transport Limited:** $$ R = h_g \cdot (C_g - C_s) $$ **Combined (Grove Model):** $$ R = \frac{k_s \cdot C_g}{1 + \frac{k_s}{h_g}} $$ - $R$ — Deposition rate (nm/min) - $k_s$ — Surface reaction rate constant (m/s) - $h_g$ — Gas-phase mass transfer coefficient (m/s) - $C_g$ — Bulk gas concentration (mol/m³) - $C_s$ — Surface concentration (mol/m³) #### 2.2.2 Arrhenius Temperature Dependence $$ k_s = A \cdot \exp\left(-\frac{E_a}{k_B T}\right) $$ - $A$ — Pre-exponential factor - $E_a$ — Activation energy (eV or J) - $k_B$ — Boltzmann constant ($1.38 \times 10^{-23}$ J/K) - $T$ — Temperature (K) #### 2.2.3 Step Coverage **Conformality Factor:** $$ SC = \frac{t_{sidewall}}{t_{top}} \times 100\% $$ **Aspect Ratio Dependence:** $$ SC \approx \frac{1}{1 + \beta \cdot AR} $$ - $AR$ — Aspect ratio (depth/width) - $\beta$ — Process-dependent constant ### 2.3 Physical Vapor Deposition (PVD) #### 2.3.1 Sputtering Yield **Sigmund Formula:** $$ Y = \frac{3\alpha}{4\pi^2} \cdot \frac{4 M_1 M_2}{(M_1 + M_2)^2} \cdot \frac{E}{U_s} $$ - $Y$ — Sputtering yield (atoms/ion) - $M_1, M_2$ — Ion and target atomic masses - $E$ — Ion energy (eV) - $U_s$ — Surface binding energy (eV) - $\alpha$ — Momentum transfer efficiency factor #### 2.3.2 Deposition Rate $$ R_{dep} = \frac{J \cdot Y \cdot M_{target}}{N_A \cdot \rho_{film} \cdot A} $$ - $J$ — Ion current density (ions/m²·s) - $M_{target}$ — Target molar mass (g/mol) - $N_A$ — Avogadro's number - $\rho_{film}$ — Film density (g/cm³) - $A$ — Deposition area (m²) ### 2.4 Plasma Etching #### 2.4.1 Etch Rate **Arrhenius Form:** $$ ER = A \cdot [F]^n \cdot \exp\left(-\frac{E_a}{k_B T}\right) $$ - $ER$ — Etch rate (nm/min) - $[F]$ — Etchant species concentration - $n$ — Reaction order - $E_a$ — Activation energy - $T$ — Wafer temperature (K) #### 2.4.2 Ion Energy Distribution **Maxwell-Boltzmann (Thermal Ions):** $$ f(E) = \frac{2\pi}{(\pi k_B T_e)^{3/2}} \cdot \sqrt{E} \cdot \exp\left(-\frac{E}{k_B T_e}\right) $$ - $T_e$ — Electron temperature (eV or K) #### 2.4.3 Selectivity $$ S = \frac{ER_{target}}{ER_{mask}} $$ #### 2.4.4 Anisotropy $$ A_f = 1 - \frac{ER_{lateral}}{ER_{vertical}} $$ - $A_f = 1$ — Perfectly anisotropic - $A_f = 0$ — Isotropic ### 2.5 Ion Implantation #### 2.5.1 Range Distribution (Gaussian Approximation) $$ N(x) = \frac{\Phi}{\sqrt{2\pi} \Delta R_p} \cdot \exp\left[-\frac{(x - R_p)^2}{2 \Delta R_p^2}\right] $$ - $N(x)$ — Dopant concentration at depth $x$ (atoms/cm³) - $\Phi$ — Implant dose (atoms/cm²) - $R_p$ — Projected range (nm) - $\Delta R_p$ — Range straggle (nm) #### 2.5.2 Projected Range (LSS Theory) $$ R_p \approx \frac{E}{S_n(E) + S_e(E)} $$ - $S_n(E)$ — Nuclear stopping power - $S_e(E)$ — Electronic stopping power #### 2.5.3 Channeling Effect $$ \psi_c = \sqrt{\frac{2 Z_1 Z_2 e^2}{4\pi \epsilon_0 E d}} $$ - $\psi_c$ — Critical channeling angle (rad) - $Z_1, Z_2$ — Atomic numbers of ion and target - $d$ — Interplanar spacing ### 2.6 Chemical Mechanical Planarization (CMP) #### 2.6.1 Preston Equation $$ RR = K_p \cdot P \cdot V $$ - $RR$ — Removal rate (nm/min) - $K_p$ — Preston coefficient (m²/N) - $P$ — Applied pressure (Pa) - $V$ — Relative velocity (m/s) #### 2.6.2 Contact Mechanics (Hertzian) $$ P_{contact} = \frac{4E^*}{3\pi} \cdot \sqrt{\frac{a}{R}} $$ - $E^*$ — Effective elastic modulus - $a$ — Contact radius - $R$ — Particle radius #### 2.6.3 Planarization Efficiency $$ PE = \frac{Step_{initial} - Step_{final}}{Step_{initial}} \times 100\% $$ ## 3. Metrology Mathematics ### 3.1 Scatterometry (OCD) #### 3.1.1 Rigorous Coupled-Wave Analysis (RCWA) **Maxwell's Equations:** $$ \nabla \times \vec{E} = -\mu_0 \frac{\partial \vec{H}}{\partial t} $$ $$ \nabla \times \vec{H} = \epsilon \frac{\partial \vec{E}}{\partial t} $$ **Fourier Expansion of Permittivity:** $$ \epsilon(x) = \sum_{m=-\infty}^{\infty} \epsilon_m \exp\left(i \frac{2\pi m}{\Lambda} x\right) $$ - $\Lambda$ — Grating period #### 3.1.2 Diffraction Efficiency $$ DE_m = \frac{I_m}{I_0} = |r_m|^2 $$ - $DE_m$ — Diffraction efficiency of $m$-th order - $r_m$ — Complex reflection coefficient ### 3.2 Ellipsometry #### 3.2.1 Fundamental Equation $$ \rho = \tan(\Psi) \cdot e^{i\Delta} = \frac{r_p}{r_s} $$ - $\Psi$ — Amplitude ratio angle - $\Delta$ — Phase difference - $r_p, r_s$ — Complex reflection coefficients (p and s polarizations) #### 3.2.2 Film Thickness (Single Layer) $$ d = \frac{\lambda}{4\pi n_1 \cos\theta_1} \cdot \left(m\pi + \phi\right) $$ - $d$ — Film thickness (nm) - $n_1$ — Film refractive index - $\theta_1$ — Refraction angle in film - $m$ — Interference order - $\phi$ — Phase shift from interfaces #### 3.2.3 Fresnel Coefficients $$ r_p = \frac{n_2 \cos\theta_1 - n_1 \cos\theta_2}{n_2 \cos\theta_1 + n_1 \cos\theta_2} $$ $$ r_s = \frac{n_1 \cos\theta_1 - n_2 \cos\theta_2}{n_1 \cos\theta_1 + n_2 \cos\theta_2} $$ ### 3.3 Atomic Force Microscopy (AFM) #### 3.3.1 Cantilever Dynamics **Simple Harmonic Oscillator:** $$ m \frac{d^2 z}{dt^2} + \gamma \frac{dz}{dt} + k z = F_{tip-sample} $$ - $m$ — Effective mass - $\gamma$ — Damping coefficient - $k$ — Spring constant (N/m) - $F_{tip-sample}$ — Tip-sample interaction force #### 3.3.2 Resonance Frequency $$ f_0 = \frac{1}{2\pi} \sqrt{\frac{k}{m_{eff}}} $$ #### 3.3.3 Tip-Sample Forces (Lennard-Jones) $$ F(r) = \frac{A}{r^{13}} - \frac{B}{r^7} $$ - $A, B$ — Material-dependent constants - $r$ — Tip-sample separation ### 3.4 Statistical Process Control (SPC) #### 3.4.1 Process Capability Index $$ C_p = \frac{USL - LSL}{6\sigma} $$ $$ C_{pk} = \min\left(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma}\right) $$ - $USL$ — Upper specification limit - $LSL$ — Lower specification limit - $\mu$ — Process mean - $\sigma$ — Process standard deviation #### 3.4.2 Control Limits $$ UCL = \bar{X} + 3\sigma $$ $$ LCL = \bar{X} - 3\sigma $$ #### 3.4.3 Six Sigma Metrics $$ DPMO = \frac{Number\ of\ Defects}{Number\ of\ Opportunities} \times 10^6 $$ **Sigma Level Conversion:** | Sigma Level | DPMO | Yield | |:------------|:-----|:------| | 3σ | 66,807 | 93.32% | | 4σ | 6,210 | 99.38% | | 5σ | 233 | 99.977% | | 6σ | 3.4 | 99.99966% | ## 4. Facility Modeling ### 4.1 Thermal Management #### 4.1.1 Heat Balance $$ \dot{Q}_{in} = \dot{Q}_{process} + \dot{Q}_{losses} + mc_p\frac{dT}{dt} $$ - $\dot{Q}_{in}$ — Heat input rate (W) - $\dot{Q}_{process}$ — Process heat load (W) - $\dot{Q}_{losses}$ — Heat losses (W) - $m$ — Thermal mass (kg) - $c_p$ — Specific heat (J/kg·K) #### 4.1.2 Thermal Resistance Network $$ R_{th} = \frac{\Delta T}{\dot{Q}} = \frac{L}{kA} $$ - $R_{th}$ — Thermal resistance (K/W) - $L$ — Conduction path length (m) - $k$ — Thermal conductivity (W/m·K) - $A$ — Cross-sectional area (m²) #### 4.1.3 Cooling Capacity $$ \dot{Q}_{cooling} = \dot{m} \cdot c_p \cdot \Delta T $$ - $\dot{m}$ — Mass flow rate (kg/s) - $\Delta T$ — Temperature difference (K) ### 4.2 Vibration Isolation #### 4.2.1 Transmissibility $$ T = \frac{1}{\sqrt{(1-r^2)^2 + (2\zeta r)^2}} $$ - $T$ — Transmissibility ratio - $r = \frac{\omega}{\omega_n}$ — Frequency ratio - $\zeta$ — Damping ratio - $\omega$ — Excitation frequency (rad/s) - $\omega_n$ — Natural frequency (rad/s) #### 4.2.2 Natural Frequency $$ \omega_n = \sqrt{\frac{k}{m}} = 2\pi f_n $$ $$ f_n = \frac{1}{2\pi}\sqrt{\frac{k}{m}} $$ #### 4.2.3 Isolation Efficiency $$ IE = \left(1 - T\right) \times 100\% $$ **Design Rule:** For effective isolation, $r > \sqrt{2}$ (frequency ratio > 1.414) ### 4.3 Ultra-Pure Water (UPW) Systems #### 4.3.1 Resistivity $$ \rho = \frac{1}{\sigma} = \frac{1}{\sum_i \lambda_i c_i} $$ - $\rho$ — Resistivity (Ω·cm) - $\sigma$ — Conductivity (S/cm) - $\lambda_i$ — Ionic equivalent conductance (S·cm²/mol) - $c_i$ — Ion concentration (mol/cm³) **Target Specification:** 18.2 MΩ·cm at 25°C (theoretical maximum for pure water) #### 4.3.2 Total Organic Carbon (TOC) $$ TOC = \frac{\Delta CO_2 \times 12}{44 \times V_{sample}} $$ - $\Delta CO_2$ — CO₂ generated from oxidation (µg) - $V_{sample}$ — Sample volume (L) - Target: < 1 ppb for advanced nodes #### 4.3.3 Particle Concentration $$ N = \frac{Counts}{V_{sampled} \times Efficiency} $$ - Specification: < 1 particle/mL at ≥ 50 nm ### 4.4 Gas Delivery Systems #### 4.4.1 Mass Flow Rate $$ \dot{m} = \rho \cdot Q = \frac{P \cdot Q \cdot M}{R \cdot T} $$ - $\dot{m}$ — Mass flow rate (kg/s) - $Q$ — Volumetric flow rate (m³/s) - $P$ — Pressure (Pa) - $M$ — Molar mass (kg/mol) - $R$ — Universal gas constant (8.314 J/mol·K) #### 4.4.2 Pressure Drop (Hagen-Poiseuille) $$ \Delta P = \frac{128 \mu L Q}{\pi d^4} $$ - $L$ — Pipe length (m) - $d$ — Pipe diameter (m) - $\mu$ — Dynamic viscosity (Pa·s) #### 4.4.3 Gas Purity $$ Purity = \left(1 - \frac{\sum Impurities}{Total}\right) \times 100\% $$ - Typical requirement: 99.9999% (6N) to 99.99999999% (10N) ## 5. Yield Modeling ### 5.1 Defect-Limited Yield #### 5.1.1 Poisson Model (Random Defects) $$ Y = e^{-D_0 \cdot A} $$ - $Y$ — Die yield (0 to 1) - $D_0$ — Defect density (defects/cm²) - $A$ — Die area (cm²) #### 5.1.2 Negative Binomial (Clustered Defects) $$ Y = \left(1 + \frac{D_0 \cdot A}{\alpha}\right)^{-\alpha} $$ - $\alpha$ — Clustering parameter (α → ∞ approaches Poisson) #### 5.1.3 Murphy's Model $$ Y = \left(\frac{1 - e^{-D_0 A}}{D_0 A}\right)^2 $$ #### 5.1.4 Seeds Model $$ Y = e^{-\sqrt{D_0 A}} $$ ### 5.2 Parametric Yield #### 5.2.1 Gaussian Distribution Model $$ Y_p = \Phi\left(\frac{USL - \mu}{\sigma}\right) - \Phi\left(\frac{LSL - \mu}{\sigma}\right) $$ - $\Phi$ — Cumulative standard normal distribution function #### 5.2.2 Combined Yield $$ Y_{total} = Y_{defect} \times Y_{parametric} \times Y_{packaging} $$ #### 5.2.3 Learning Curve $$ D_0(t) = D_{0,initial} \cdot \left(\frac{V(t)}{V_0}\right)^{-\beta} $$ - $V(t)$ — Cumulative production volume - $\beta$ — Learning rate exponent (typically 0.3–0.5) ## 6. Reference Tables ### 6.1 Process Equations Quick Reference | **Domain** | **Key Equation** | **Primary Variables** | |:-----------|:-----------------|:----------------------| | Cleanroom Particles | $\frac{\partial C}{\partial t} + \nabla \cdot (C\vec{v}) = S - \lambda C$ | $C$, $\vec{v}$, $S$, $\lambda$ | | Airflow | $\rho(\frac{\partial \vec{v}}{\partial t} + \vec{v} \cdot \nabla\vec{v}) = -\nabla p + \mu \nabla^2 \vec{v}$ | $\rho$, $\vec{v}$, $p$, $\mu$ | | Lithography CD | $R = k_1 \frac{\lambda}{NA}$ | $k_1$, $\lambda$, $NA$ | | CVD Growth | $R = \frac{k_s C_g}{1 + k_s/h_g}$ | $k_s$, $C_g$, $h_g$ | | Etch Rate | $ER = A[F]^n \exp(-E_a/k_B T)$ | $[F]$, $E_a$, $T$ | | CMP | $RR = K_p \cdot P \cdot V$ | $K_p$, $P$, $V$ | | Ellipsometry | $\rho = \tan(\Psi) e^{i\Delta}$ | $\Psi$, $\Delta$, $r_p$, $r_s$ | | Process Capability | $C_{pk} = \min(\frac{USL-\mu}{3\sigma}, \frac{\mu-LSL}{3\sigma})$ | $USL$, $LSL$, $\mu$, $\sigma$ | | Yield (Poisson) | $Y = e^{-D_0 A}$ | $D_0$, $A$ | | Vibration | $T = \frac{1}{\sqrt{(1-r^2)^2 + (2\zeta r)^2}}$ | $r$, $\zeta$ | ### 6.2 Physical Constants | **Constant** | **Symbol** | **Value** | **Units** | |:-------------|:-----------|:----------|:----------| | Boltzmann constant | $k_B$ | $1.381 \times 10^{-23}$ | J/K | | Avogadro's number | $N_A$ | $6.022 \times 10^{23}$ | mol⁻¹ | | Elementary charge | $e$ | $1.602 \times 10^{-19}$ | C | | Permittivity of vacuum | $\epsilon_0$ | $8.854 \times 10^{-12}$ | F/m | | Permeability of vacuum | $\mu_0$ | $4\pi \times 10^{-7}$ | H/m | | Gas constant | $R$ | $8.314$ | J/(mol·K) | | Planck constant | $h$ | $6.626 \times 10^{-34}$ | J·s | ### 6.3 Cleanroom Classification (ISO 14644-1) | **ISO Class** | **Max Particles ≥ 0.1 µm** | **Max Particles ≥ 0.5 µm** | **Typical Application** | |:--------------|:---------------------------|:---------------------------|:------------------------| | ISO 1 | 10 | — | Research, EUV | | ISO 2 | 100 | — | Advanced lithography | | ISO 3 | 1,000 | 35 | Leading-edge fabs | | ISO 4 | 10,000 | 352 | Advanced manufacturing | | ISO 5 | 100,000 | 3,520 | Standard IC production | | ISO 6 | 1,000,000 | 35,200 | Assembly, packaging | *Units: particles/m³* ### Math Syntax Reference | **Type** | **Syntax** | **Example** | |:---------|:-----------|:------------| | Inline math | `$...$` | `$E = mc^2$` → $E = mc^2$ | | Display math | `$$...$$` | `$$\int_0^\infty e^{-x}dx$$` | | Fractions | `\frac{a}{b}` | $\frac{a}{b}$ | | Subscript | `x_i` | $x_i$ | | Superscript | `x^2` | $x^2$ | | Greek letters | `\alpha, \beta, \gamma` | $\alpha, \beta, \gamma$ | | Partial derivative | `\frac{\partial f}{\partial x}` | $\frac{\partial f}{\partial x}$ | | Vectors | `\vec{v}` | $\vec{v}$ | | Matrices | `\begin{bmatrix}...\end{bmatrix}` | — |

cleanroom,facility

Controlled environment with minimal particles for semiconductor manufacturing.

clearml, mlops

End-to-end ML platform.

clearml,mlops,end to end

ClearML is end-to-end MLOps platform. Experiment tracking, orchestration, serving.

cleaving,metrology

Split crystal along natural planes.

clebsch-gordan, graph neural networks

Clebsch-Gordan tensor products combine spherical harmonic representations maintaining equivariance.

clevr,evaluation

Compositional visual reasoning.

cli,tooling,command line app

I can design and implement CLI tools, including argument parsing, help messages, and good ergonomics.

click model, recommendation systems

Click models estimate true relevance from biased user interactions accounting for examination probability.

client selection strategies, federated learning

Choose which clients participate.

climate-fever, evaluation

Climate change fact-checking.

clinical note generation,healthcare ai

Create structured clinical notes.

clinical note summarization, healthcare ai

Summarize medical records.

clinical text de-identification, healthcare ai

Remove PHI from clinical text.

clinical trial matching, healthcare ai

Match patients to trials using NLP.

clinical trial matching,healthcare ai

Match patients to appropriate trials.

clinical trial protocol generation, healthcare ai

Draft trial protocols.

clinical,ehr,healthcare

Clinical AI processes EHR data. Prediction, NLP on notes, decision support. HIPAA compliance critical.

clip (contrastive language-image pre-training),clip,contrastive language-image pre-training,multimodal ai

Align image and text embeddings using contrastive learning.

clip guidance,generative models

Use CLIP scores to guide image generation.

clip loss for optimization, clip, generative models

Optimize towards CLIP similarity.

clip score, clip, evaluation

Vision-language similarity metric.

clip training methodology, clip, multimodal ai

Contrastive learning for vision-language.

clip-guided generation, generative models

Use CLIP to guide image generation.

clip,contrastive,multimodal

CLIP trains vision and language encoders jointly. Contrastive learning. Zero-shot image classification.

clip,embedding,image search

CLIP embeds images and text in same space. Enables text-to-image search and zero-shot image classification.

clock domain crossing, design & verification

Clock domain crossings transfer signals between different clock domains requiring synchronization.

clock frequency,ghz,speed

Clock frequency (GHz) sets compute speed. Higher frequency = more heat. Trade-off with parallelism.

clock gating, design & verification

Clock gating disables clock to idle circuits reducing dynamic power consumption.

clock gating,design

Disable clock to idle logic to reduce dynamic power.

clock latency, design & verification

Clock latency is total delay from clock source to register.

clock skew, design & verification

Clock skew is timing difference between clock arrivals at different registers.

clock skew,design

Timing difference in clock arrival.

clock tree synthesis, design & verification

Clock tree synthesis constructs balanced distribution network minimizing skew and insertion delay.

clock tree synthesis,design

Build balanced clock distribution.

clock tree, design & verification

Clock trees distribute clock signals to registers minimizing skew through balanced routing.

clock uncertainty, design & verification

Clock uncertainty accounts for jitter and skew in timing analysis.

closed source,api,proprietary

Closed source AI: API access only. GPT-4, Claude. Control but less transparency.

closed-book qa,nlp

Answer without external documents.

closed-form continuous-time networks, neural architecture

Tractable continuous-time neural networks with guaranteed stability.

cloud training economics, business

Financial considerations for cloud training.

cloud,aws,gcp,azure

I help you reason about cloud architecture, cost trade-offs, and how to deploy AI workloads on AWS/GCP/Azure step by step.

cloze task, nlp

Fill-in-the-blank task.

cluster analysis methods, manufacturing operations

Cluster analysis groups similar observations or variables revealing patterns.

cluster analysis of defects, metrology

Find defect clusters on wafer.

cluster analysis wafer, manufacturing operations

Cluster analysis groups adjacent failing die identifying localized defect sources.

cluster analysis, data analysis

Group similar data points.

cluster detection, yield enhancement

Cluster detection algorithms locate groups of failing die indicating localized defects.

cluster tool,production

Tool with central vacuum transfer chamber and multiple process modules.

clustered federated learning, federated learning

Group similar clients.