TCAD Model Parameters

Keywords: tcad model parameters, tcad, simulation

TCAD Model Parameters are physical values used in device and process simulation — including diffusion coefficients, mobility models, recombination lifetimes, and material properties that determine simulation accuracy, requiring careful selection from literature, calibration to experiments, or ab-initio calculations for predictive modeling.

What Are TCAD Model Parameters?

• Definition: Physical constants and model coefficients used in TCAD simulations.
• Categories: Process parameters, device parameters, material properties.
• Sources: Literature, calibration, ab-initio calculations, vendor databases.
• Impact: Determine accuracy and predictive capability of simulations.

Why Parameters Matter

• Simulation Accuracy: Correct parameters essential for quantitative predictions.
• Process Optimization: Accurate parameters enable virtual process development.
• Technology Transfer: Parameter sets encode process knowledge.
• Uncertainty: Parameter uncertainty propagates to simulation results.
• Calibration: Starting point for calibration to experimental data.

Process Parameters

Diffusion:

• Diffusion Coefficient: D = D_0 · exp(-E_a / kT).
• D_0: Pre-exponential factor (cm²/s).
• E_a: Activation energy (eV).
• Species-Dependent: Different for each dopant (B, P, As, Sb).
• Concentration-Dependent: Enhanced diffusion at high concentrations.

Segregation:

• Segregation Coefficient: Ratio of dopant concentration across interface.
• Example: Si/SiO₂ interface segregation.
• Impact: Dopant redistribution during oxidation.

Oxidation:

• Deal-Grove Parameters: Linear and parabolic rate constants.
• Temperature-Dependent: Arrhenius behavior.
• Orientation-Dependent: Different rates for (100) vs. (111) silicon.

Implantation:

• Range Parameters: Projected range R_p, straggle ΔR_p.
• Channeling: Enhanced penetration along crystal axes.
• Damage: Lattice damage from ion bombardment.

Device Parameters

Mobility Models:

• Low-Field Mobility: μ_0 for electrons and holes.
• Field-Dependent: μ(E) models (Caughey-Thomas, etc.).
• Doping-Dependent: Mobility degradation at high doping.
• Temperature-Dependent: μ ∝ T^(-α).

Recombination:

• SRH Lifetime: τ_n, τ_p for Shockley-Read-Hall recombination.
• Auger Coefficients: C_n, C_p for Auger recombination.
• Surface Recombination: S_n, S_p at interfaces.

Bandgap:

• Intrinsic Bandgap: E_g(T) temperature dependence.
• Bandgap Narrowing: ΔE_g at high doping.
• Strain Effects: Bandgap modification under stress.

Tunneling:

• Effective Mass: m* for tunneling calculations.
• Barrier Height: Φ_B for metal-semiconductor, insulator barriers.

Material Properties

Thermal:

• Thermal Conductivity: κ(T) for heat transfer.
• Specific Heat: C_p for thermal capacity.
• Thermal Expansion: α for stress calculations.

Mechanical:

• Young's Modulus: E for elastic deformation.
• Poisson's Ratio: ν for stress-strain relationships.
• Yield Strength: For plastic deformation.

Electrical:

• Dielectric Constant: ε_r for insulators.
• Work Function: Φ_M for metals, Φ_S for semiconductors.
• Electron Affinity: χ for band alignment.

Parameter Sources

Literature Values:

• Textbooks: Sze, Streetman for standard parameters.
• Papers: Research papers for specific materials, conditions.
• Databases: NIST, semiconductor handbooks.
• Advantages: Readily available, peer-reviewed.
• Limitations: May not match specific process conditions.

Calibration to Experiments:

• Method: Fit parameters to match experimental measurements.
• Advantages: Accurate for specific process.
• Limitations: Time-consuming, requires experimental data.
• Use Case: Critical parameters, process-specific values.

Ab-Initio Calculations:

• Method: DFT (Density Functional Theory) calculations.
• Advantages: No experimental data needed, fundamental.
• Limitations: Computationally expensive, approximations.
• Use Case: New materials, defect properties, interfaces.

Vendor Databases:

• Source: TCAD tool vendors provide default parameter sets.
• Advantages: Integrated, tested, documented.
• Limitations: Generic, may need customization.
• Use Case: Starting point for simulations.

Parameter Sensitivity

High-Impact Parameters:

• Mobility: Strongly affects device current, speed.
• Diffusion Coefficient: Determines dopant profiles, junction depth.
• Recombination Lifetime: Affects leakage, minority carrier devices.
• Bandgap: Fundamental for all electrical properties.

Low-Impact Parameters:

• Some Material Properties: Thermal conductivity (unless thermal effects critical).
• Higher-Order Terms: Often negligible for first-order analysis.

Sensitivity Analysis:

• Method: Vary each parameter, measure impact on simulation output.
• Identify Critical: Focus calibration on high-sensitivity parameters.
• Uncertainty Propagation: Quantify how parameter uncertainty affects results.

Parameter Management

Version Control:

• Track Changes: Maintain history of parameter set modifications.
• Documentation: Record why parameters were changed.
• Branching: Different parameter sets for different processes.

Documentation:

• Source: Document where each parameter came from.
• Conditions: Record calibration conditions, temperature range, etc.
• Uncertainty: Quantify parameter uncertainties.
• Validation: Document validation against experimental data.

Database Management:

• Centralized: Maintain central parameter database.
• Access Control: Manage who can modify parameters.
• Backup: Regular backups of parameter sets.

Best Practices

Start with Literature:

• Baseline: Begin with well-established literature values.
• Validate: Check if literature values match your process.
• Calibrate: Adjust only parameters that need it.

Calibrate Systematically:

• Prioritize: Calibrate high-sensitivity parameters first.
• One at a Time: Avoid changing many parameters simultaneously.
• Validate: Test calibrated parameters on independent data.

Physical Reasonableness:

• Check Values: Ensure parameters are physically reasonable.
• Compare: Compare to literature, other processes.
• Expert Review: Have experts review parameter sets.

Uncertainty Quantification:

• Confidence Intervals: Quantify parameter uncertainties.
• Propagation: Understand how uncertainty affects predictions.
• Sensitivity: Know which parameters matter most.

Tools & Resources

• TCAD Software: Synopsys, Silvaco, Crosslight with parameter databases.
• Literature: Sze, Streetman, Grove textbooks.
• Databases: NIST, semiconductor material databases.
• Calibration Tools: Integrated parameter extraction tools.

TCAD Model Parameters are the foundation of simulation accuracy — careful selection, calibration, and management of parameters determines whether simulations provide quantitative predictions or just qualitative trends, making parameter management a critical aspect of successful TCAD-based process development and optimization.

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