The Only Native GPU Platform
For Thin Film Optimization
Accelerate your Thin Film Design Process and reduce time-to-market
while ensuring compliance with key requirements.
Technical
Capabilities
Dispersion Models
Real:
Constant:
n=A_0
Cauchy:
n(\lambda_0)=A_0+\sum_{i=1}^nB_i\lambda_0^{Ci}
Conrady:
n(\lambda_0) = A_0+\frac{A_1}{\lambda_0}+\frac{A_2}{\lambda_0^{3.5}}
Exotic:
n^2(\lambda_0) = A_0+\frac{A_1}{\lambda_0^2-A_2}+\frac{A_3(\lambda_0-A_4)}{(\lambda_0-A_4)^2+A_5}
Gases:
n(\lambda_0)-1=A_0+\sum_{i=0}^n\large\frac{B_i}{C_i-\lambda_0^{-2}}
Lorentz-Drude:
\Re(\epsilon_r(\omega)) = \epsilon_r(\infty)+\omega_p^2\sum_{i=1}^M\frac{f_i}{\omega_{0,i}^2-\omega^2+\jmath\omega\Gamma_i}
Hartmann:
n(\lambda_0) = A_0+\frac{A_1}{\lambda_0-A_2}
Hartmann-Modified:
n(\lambda_0) = A_0+\frac{A_1}{(\lambda_0-A_2)^2}
Herzberger:
n(\lambda_0) = A_0+\frac{A_1}{\lambda_0^2-0.028}+A_2(\frac{1}{\lambda_0^2-0.028})^2+A_3\lambda_0^2+A_4\lambda_0^4+A_5\lambda_0^6
Schoitt-Briot:
n^2(\lambda_0)=A_0+A_1\lambda_0^2+\sum_{i=1}^n\large\frac{B_i}{\lambda_0^{2i}}
Sellmeier:
n^2(\lambda_0)=A_0+\sum_{i=0}^n\large\frac{B_i\lambda_0^2}{\lambda_0^2-C_i}
Sellmeier-Modified:
n^2(\lambda_0)=A_0+\sum_{i=0}^n\frac{B_i\lambda_0^2}{\lambda_0^2-C_i^2}
Polynomial:
n^2(\lambda_0) = A_0+\sum_{ i = 1 }^n\large B_i\lambda_0^{ C_{ i } }
RefractiveIndexInfo:
n^2(\lambda) = A_0+\frac{B_1\lambda_0^{C_1}}{\lambda_0^2-B_2^{C_2}}+\frac{B_3\lambda_0^{C_3}}{\lambda_0^2-B_4^{C_4}}+B_5\lambda_0^{C_{5}}+B_{6}\lambda_0^{C_{6}}+B_{7}\lambda_0^{C_{7}}+B_{8}\lambda_0^{C_{8}}
Datatable
Imaginary:
Non-Absorptive:
k=0
Constant:
k=A_0
Cauchy:
k(\lambda_0)=A_0e^{A_1(\frac{1}{\lambda_0}-\frac{1}{A_2})}
Exponential:
k(\lambda_0)=A_0e^{\frac{A_1}{\lambda_0}+A_2\lambda_0}
Lorentz-Drude:
\Im(\epsilon_r(\omega)) = \epsilon_r(\infty)+\omega_p^2\sum_{i=1}^M\frac{f_i}{\omega_{0,i}^2-\omega^2+\jmath\omega\Gamma_i}
Polynomial:
k(\lambda_0)=A_0+\sum_{i=1}^nB_i\lambda_0^{Ci}
Sellmeier:
k(\lambda_0)={\frac{A_0}{A_1A_2+\frac{A_3}{\lambda_0}+\frac{1}{\lambda_0^3}}}
Datatable
Optical Characteristics
Conventional:
R — Reflectance (s-pol, p-pol, avg)
T — Transmittance (s-pol, p-pol, avg)
A — Absorptance (s-pol, p-pol, avg)
Phase:
pR — Phase Shift on Reflection (s-pol, p-pol,)
pT — Phase Shift on Transmission (s-pol, p-pol)
GDR —Group Delay on Reflection (s-pol, p-pol)
GDT —Group Delay on Transmission (s-pol, p-pol)
GDDR — Group Delay Dispersion on Reflection (s-pol, p-pol)
GDDT — Group Delay Dispersion on Transmission (s-pol, p-pol)
Layer-Specific:
LA — Layer Absorptance (s-pol, p-pol, avg)
LOA — Local Absorption (s-pol, p-pol, avg)
E — Electric Field (s-pol, p-pol, avg)
Merit Formulas
Quadratic :
F=\sqrt{\frac{1}{m}\sum_{i =1}w_i\lbrack\frac{S_i-\hat{S_i}}{\Delta_T}\rbrack^2}
Absolut:
F=\frac{1}{m}\sum_{i =1}w_i\lbrack\frac{\vert{S_i-\hat{S_i}\vert}}{\Delta_T}\rbrack
Max:
F=\max{\lbrack{w_i}\frac{\vert{S_i-\hat{S_i}\vert}}{\Delta_T}: 1..m\rbrack}
Coherence Models
Coherent Stack — All optical characteristics.
Incoherent Stack — R/T/A.
Hybrid (Mixed) Stack — R/T/A.
Algorithms
Refinement:
Adam — Adaptive Momentum Estimation
ClusterRefinement — Proprietary, optimizes clusters of layers.
Synthesis:
Global Optimization:
BranchAndBound — Global optimizer with mathematical guarantees regarding global optimality
ParticleSwarmOptimization — A population-based global optimizer that guides a swarm of candidate solutions through the search space using individual and social intelligence
Stochastic — Stochastic global optimizer