Device Physics & Mathematical Modeling

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Device Physics & Mathematical Modeling

1. Fundamental Mathematical Structure

Semiconductor modeling is built on coupled nonlinear partial differential equations spanning multiple scales:

Scale	Methods	Typical Equations
Quantum (< 1 nm)	DFT, Schrödinger	$H\psi = E\psi$
Atomistic (1–100 nm)	MD, Kinetic Monte Carlo	Newton's equations, master equations
Continuum (nm–mm)	Drift-diffusion, FEM	PDEs (Poisson, continuity, heat)
Circuit	SPICE	ODEs, compact models

Multiscale Hierarchy

The mathematics forms a hierarchy of models through successive averaging:

\boxed{\text{Schrödinger} \xrightarrow{\text{averaging}} \text{Boltzmann} \xrightarrow{\text{moments}} \text{Drift-Diffusion} \xrightarrow{\text{fitting}} \text{Compact Models}}

2. Process Physics & Models

2.1 Oxidation: Deal-Grove Model

Thermal oxidation of silicon follows linear-parabolic kinetics :

\frac{dx_{ox}}{dt} = \frac{B}{A + 2x_{ox}}

where:

$x_{ox}$ = oxide thickness
$B/A$ = linear rate constant (surface-reaction limited)
$B$ = parabolic rate constant (diffusion limited)

Limiting Cases:

Thin oxide (reaction-limited):

x_{ox} \approx \frac{B}{A} \cdot t

Thick oxide (diffusion-limited):

x_{ox} \approx \sqrt{B \cdot t}

Physical Mechanism:

1. O₂ transport from gas to oxide surface 2. O₂ diffusion through growing SiO₂ layer 3. Reaction at Si/SiO₂ interface: $\text{Si} + \text{O}_2 \rightarrow \text{SiO}_2$

Note: This is a Stefan problem (moving boundary PDE).

2.2 Diffusion: Fick's Laws

Dopant redistribution follows Fick's second law :

\frac{\partial C}{\partial t} = \nabla \cdot \left( D(C, T) \nabla C \right)

For constant $D$ in 1D:

\frac{\partial C}{\partial t} = D \frac{\partial^2 C}{\partial x^2}

Analytical Solutions (1D, constant D):

Constant surface concentration (infinite source):

C(x,t) = C_s \cdot \text{erfc}\left( \frac{x}{2\sqrt{Dt}} \right)

Limited source (e.g., implant drive-in):

C(x,t) = \frac{Q}{\sqrt{\pi D t}} \exp\left( -\frac{x^2}{4Dt} \right)

where $Q$ = dose (atoms/cm²)

Complications at High Concentrations:

Concentration-dependent diffusivity: $D = D(C)$
Electric field effects: Charged point defects create internal fields
Vacancy/interstitial mechanisms: Different diffusion pathways

\frac{\partial C}{\partial t} = \frac{\partial}{\partial x}\left[ D(C) \frac{\partial C}{\partial x} \right] + \mu C \frac{\partial \phi}{\partial x}

2.3 Ion Implantation: Range Theory

The implanted dopant profile is approximately Gaussian :

C(x) = \frac{\Phi}{\sqrt{2\pi} \Delta R_p} \exp\left( -\frac{(x - R_p)^2}{2 (\Delta R_p)^2} \right)

where:

$\Phi$ = implant dose (ions/cm²)
$R_p$ = projected range (mean depth)
$\Delta R_p$ = straggle (standard deviation)

LSS Theory (Lindhard-Scharff-Schiøtt) predicts stopping power:

-\frac{dE}{dx} = N \left[ S_n(E) + S_e(E) \right]

where:

$S_n(E)$ = nuclear stopping power (dominant at low energy)
$S_e(E)$ = electronic stopping power (dominant at high energy)
$N$ = target atomic density

For asymmetric profiles , the Pearson IV distribution is used:

C(x) = \frac{\Phi \cdot K}{\Delta R_p} \left[ 1 + \left( \frac{x - R_p}{a} \right)^2 \right]^{-m} \exp\left[ - u \arctan\left( \frac{x - R_p}{a} \right) \right]

Modern approach: Monte Carlo codes (SRIM/TRIM) for accurate profiles including channeling effects.

2.4 Lithography: Optical Imaging

Aerial image formation follows Hopkins' partially coherent imaging theory :

I(\mathbf{r}) = \iint TCC(f, f') \cdot \tilde{M}(f) \cdot \tilde{M}^*(f') \cdot e^{2\pi i (f - f') \cdot \mathbf{r}} \, df \, df'

where:

$TCC$ = Transmission Cross-Coefficient
$\tilde{M}(f)$ = mask spectrum (Fourier transform of mask pattern)
$\mathbf{r}$ = position in image plane

Fundamental Limits:

Rayleigh resolution criterion:

CD_{\min} = k_1 \frac{\lambda}{NA}

Depth of focus:

DOF = k_2 \frac{\lambda}{NA^2}

where:

$\lambda$ = wavelength (193 nm for ArF, 13.5 nm for EUV)
$NA$ = numerical aperture
$k_1, k_2$ = process-dependent factors

Resist Modeling — Dill Equations:

\frac{\partial M}{\partial t} = -C \cdot I(z) \cdot M

\frac{dI}{dz} = -(\alpha M + \beta) I

where $M$ = photoactive compound concentration.

2.5 Etching & Deposition: Surface Evolution

Topography evolution is modeled with the level set method :

\frac{\partial \phi}{\partial t} + V |\nabla \phi| = 0

where:

$\phi(\mathbf{r}, t) = 0$ defines the surface
$V$ = local velocity (etch rate or deposition rate)

For anisotropic etching:

V = V(\theta, \phi, \text{ion flux}, \text{chemistry})

CVD in High Aspect Ratio Features:

Knudsen diffusion limits step coverage:

\frac{\partial C}{\partial t} = D_K \nabla^2 C - k_s C \cdot \delta_{\text{surface}}

where:

$D_K = \frac{d}{3}\sqrt{\frac{8k_BT}{\pi m}}$ (Knudsen diffusivity)
$d$ = feature width
$k_s$ = surface reaction rate

ALD (Atomic Layer Deposition):

Self-limiting surface reactions follow Langmuir kinetics:

\theta = \frac{K \cdot P}{1 + K \cdot P}

where $\theta$ = surface coverage, $P$ = precursor partial pressure.

3. Device Physics: Semiconductor Equations

The core mathematical framework for device simulation consists of three coupled PDEs :

3.1 Poisson's Equation (Electrostatics)

\nabla \cdot (\varepsilon \nabla \psi) = -q \left( p - n + N_D^+ - N_A^- \right)

where:

$\psi$ = electrostatic potential
$n, p$ = electron and hole concentrations
$N_D^+, N_A^-$ = ionized donor and acceptor concentrations

3.2 Continuity Equations (Carrier Conservation)

Electrons:

\frac{\partial n}{\partial t} = \frac{1}{q} \nabla \cdot \mathbf{J}_n + G - R

Holes:

\frac{\partial p}{\partial t} = -\frac{1}{q} \nabla \cdot \mathbf{J}_p + G - R

where:

$G$ = generation rate
$R$ = recombination rate

3.3 Current Density Equations (Transport)

Drift-Diffusion Model:

\mathbf{J}_n = q \mu_n n \mathbf{E} + q D_n \nabla n

\mathbf{J}_p = q \mu_p p \mathbf{E} - q D_p \nabla p

Einstein Relation:

\frac{D_n}{\mu_n} = \frac{D_p}{\mu_p} = \frac{k_B T}{q} = V_T

3.4 Recombination Models

Shockley-Read-Hall (SRH) Recombination:

R_{SRH} = \frac{np - n_i^2}{\tau_p (n + n_1) + \tau_n (p + p_1)}

Auger Recombination:

R_{Auger} = C_n n (np - n_i^2) + C_p p (np - n_i^2)

Radiative Recombination:

R_{rad} = B (np - n_i^2)

3.5 MOSFET Physics

Threshold Voltage:

V_T = V_{FB} + 2\phi_B + \frac{\sqrt{2 \varepsilon_{Si} q N_A (2\phi_B)}}{C_{ox}}

where:

$V_{FB}$ = flat-band voltage
$\phi_B = \frac{k_BT}{q} \ln\left(\frac{N_A}{n_i}\right)$ = bulk potential
$C_{ox} = \frac{\varepsilon_{ox}}{t_{ox}}$ = oxide capacitance

Drain Current (Gradual Channel Approximation):

Linear region ($V_{DS} < V_{GS} - V_T$):

I_D = \frac{W}{L} \mu_n C_{ox} \left[ (V_{GS} - V_T) V_{DS} - \frac{V_{DS}^2}{2} \right]

Saturation region ($V_{DS} \geq V_{GS} - V_T$):

I_D = \frac{W}{2L} \mu_n C_{ox} (V_{GS} - V_T)^2

4. Quantum Effects at Nanoscale

For modern devices with gate lengths $L_g < 10$ nm, classical models fail.

4.1 Quantum Confinement

In thin silicon channels, carrier energy becomes quantized :

E_n = \frac{\hbar^2 \pi^2 n^2}{2 m^* t_{Si}^2}

where:

$n$ = quantum number (1, 2, 3, ...)
$m^*$ = effective mass
$t_{Si}$ = silicon body thickness

Effects:

Increased threshold voltage
Modified density of states: $g_{2D}(E) = \frac{m^*}{\pi \hbar^2}$ (step function)

4.2 Quantum Tunneling

Gate Leakage (Direct Tunneling):

WKB approximation:

T \approx \exp\left( -2 \int_0^{t_{ox}} \kappa(x) \, dx \right)

where $\kappa = \sqrt{\frac{2m^*(\Phi_B - E)}{\hbar^2}}$

Source-Drain Tunneling:

Limits OFF-state current in ultra-short channels.

Band-to-Band Tunneling:

Enables Tunnel FETs (TFETs):

I_{BTBT} \propto \exp\left( -\frac{4\sqrt{2m^*} E_g^{3/2}}{3q\hbar |\mathbf{E}|} \right)

4.3 Ballistic Transport

When channel length $L < \lambda_{mfp}$ (mean free path), the Landauer formalism applies:

I = \frac{2q}{h} \int T(E) \left[ f_S(E) - f_D(E) \right] dE

where:

$T(E)$ = transmission probability
$f_S, f_D$ = source and drain Fermi functions

Ballistic Conductance Quantum:

G_0 = \frac{2q^2}{h} \approx 77.5 \, \mu\text{S}

4.4 NEGF Formalism

The Non-Equilibrium Green's Function method is the gold standard for quantum transport:

G^R = \left[ EI - H - \Sigma_1 - \Sigma_2 \right]^{-1}

where:

$H$ = device Hamiltonian
$\Sigma_1, \Sigma_2$ = contact self-energies
$G^R$ = retarded Green's function

Observables:

Electron density: $n(\mathbf{r}) = -\frac{1}{\pi} \text{Im}[G^<(\mathbf{r}, \mathbf{r}; E)]$
Current: $I = \frac{q}{h} \text{Tr}[\Gamma_1 G^R \Gamma_2 G^A]$

5. Numerical Methods

5.1 Discretization: Scharfetter-Gummel Scheme

The drift-diffusion current requires special treatment to avoid numerical instability:

J_{n,i+1/2} = \frac{q D_n}{h} \left[ n_{i+1} B\left( -\frac{\Delta \psi}{V_T} \right) - n_i B\left( \frac{\Delta \psi}{V_T} \right) \right]

where the Bernoulli function is:

B(x) = \frac{x}{e^x - 1}

Properties:

$B(0) = 1$
$B(x) \to 0$ as $x \to \infty$
$B(-x) = x + B(x)$

5.2 Solution Strategies

Gummel Iteration (Decoupled):

1. Solve Poisson for $\psi$ (fixed $n$, $p$) 2. Solve electron continuity for $n$ (fixed $\psi$, $p$) 3. Solve hole continuity for $p$ (fixed $\psi$, $n$) 4. Repeat until convergence

Newton-Raphson (Fully Coupled):

Solve the Jacobian system:

\begin{pmatrix} \frac{\partial F_\psi}{\partial \psi} & \frac{\partial F_\psi}{\partial n} & \frac{\partial F_\psi}{\partial p} \\ \frac{\partial F_n}{\partial \psi} & \frac{\partial F_n}{\partial n} & \frac{\partial F_n}{\partial p} \\ \frac{\partial F_p}{\partial \psi} & \frac{\partial F_p}{\partial n} & \frac{\partial F_p}{\partial p} \end{pmatrix} \begin{pmatrix} \delta \psi \\ \delta n \\ \delta p \end{pmatrix} = - \begin{pmatrix} F_\psi \\ F_n \\ F_p \end{pmatrix}

5.3 Time Integration

Stiffness Problem:

Time scales span ~15 orders of magnitude:

Process	Time Scale
Carrier relaxation	~ps
Thermal response	~μs–ms
Dopant diffusion	min–hours

Solution: Use implicit methods (Backward Euler, BDF).

5.4 Mesh Requirements

Debye Length Constraint:

The mesh must resolve the Debye length:

\lambda_D = \sqrt{\frac{\varepsilon k_B T}{q^2 n}}

For $n = 10^{18}$ cm⁻³: $\lambda_D \approx 4$ nm

Adaptive Mesh Refinement:

Refine near junctions, interfaces, corners
Coarsen in bulk regions
Use Delaunay triangulation for quality

6. Compact Models for Circuit Simulation

For SPICE-level simulation, physics is abstracted into algebraic/empirical equations.

Industry Standard Models

Model	Device	Key Features
BSIM4	Planar MOSFET	~300 parameters, channel length modulation
BSIM-CMG	FinFET	Tri-gate geometry, quantum effects
BSIM-GAA	Nanosheet	Stacked channels, sheet width
PSP	Bulk MOSFET	Surface-potential-based

Key Physics Captured

Short-channel effects: DIBL, $V_T$ roll-off
Quantum corrections: Inversion layer quantization
Mobility degradation: Surface scattering, velocity saturation
Parasitic effects: Series resistance, overlap capacitance
Variability: Statistical mismatch models

Threshold Voltage Variability (Pelgrom's Law)

\sigma_{V_T} = \frac{A_{VT}}{\sqrt{W \cdot L}}

where $A_{VT}$ is a technology-dependent constant.

7. TCAD Co-Simulation Workflow

The complete semiconductor design flow:

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Key Challenge: Propagating variability through the entire chain:

Line Edge Roughness (LER)
Random Dopant Fluctuation (RDF)
Work function variation
Thickness variations

8. Mathematical Frontiers

8.1 Machine Learning + Physics

Physics-Informed Neural Networks (PINNs):

\mathcal{L} = \mathcal{L}_{data} + \lambda \mathcal{L}_{physics}

where $\mathcal{L}_{physics}$ enforces PDE residuals.

Surrogate models for expensive TCAD simulations
Inverse design and topology optimization
Defect prediction in manufacturing

8.2 Stochastic Modeling

Random Dopant Fluctuation:

\sigma_{V_T} \propto \frac{t_{ox}}{\sqrt{W \cdot L \cdot N_A}}

Approaches:

Atomistic Monte Carlo (place individual dopants)
Statistical impedance field method
Compact model statistical extensions

8.3 Multiphysics Coupling

Electro-Thermal Self-Heating:

\rho C_p \frac{\partial T}{\partial t} = \nabla \cdot (\kappa \nabla T) + \mathbf{J} \cdot \mathbf{E}

Stress Effects on Mobility (Piezoresistance):

\frac{\Delta \mu}{\mu_0} = \pi_L \sigma_L + \pi_T \sigma_T

Electromigration in Interconnects:

\mathbf{J}_{atoms} = \frac{D C}{k_B T} \left( Z^* q \mathbf{E} - \Omega \nabla \sigma \right)

8.4 Atomistic-Continuum Bridging

Strategies:

Coarse-graining from MD/DFT
Density gradient quantum corrections:

V_{QM} = \frac{\gamma \hbar^2}{12 m^*} \frac{\nabla^2 \sqrt{n}}{\sqrt{n}}

Hybrid methods: atomistic core + continuum far-field

The mathematics of semiconductor manufacturing and device physics encompasses:

\boxed{ \begin{aligned} &\text{Process:} && \text{Stefan problems, diffusion PDEs, reaction kinetics} \\ &\text{Device:} && \text{Coupled Poisson + continuity equations} \\ &\text{Quantum:} && \text{Schrödinger, NEGF, tunneling} \\ &\text{Numerical:} && \text{FEM/FDM, Scharfetter-Gummel, Newton iteration} \\ &\text{Circuit:} && \text{Compact models (BSIM), variability statistics} \end{aligned} }

Each level trades accuracy for computational tractability . The art lies in knowing when each approximation breaks down—and modern scaling is pushing us toward the quantum limit where classical continuum models become inadequate.

device physics mathematicsdevice physics mathsemiconductor device physicsTCAD modelingdrift diffusionpoisson equationmosfet physicsquantum effects

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