In this paper, a novel baseband macromodeling framework for linear passive photonic circuits is proposed, which is able to build accurate and compact models while taking into account the nonidealities, such as higher order dispersion and wavelength-dependent losses of the circuits. Compared to a previous modeling method based on the vector fitting algorithm, the proposed modeling approach introduces a novel complex vector fitting technique. It can generate a half-size state-space model for the same applications, thereby achieving a major improvement in efficiency of the time-domain simulations. The proposed modeling framework requires only measured or simulated scattering parameters as input, which are widely used to represent linear and passive systems. Three photonic circuits are studied to demonstrate the accuracy and efficiency of the proposed technique.

In this paper, a novel modeling and simulation method for general linear, time-invariant, passive photonic devices and circuits is proposed. This technique, starting from the scattering parameters of the photonic system under study, builds a baseband equivalent state-space model that splits the optical carrier frequency and operates at baseband, thereby significantly reducing the modeling and simulation complexity without losing accuracy. Indeed, it is possible to analytically reconstruct the port signals of the photonic system under study starting from the time-domain simulation of the corresponding baseband equivalent model. However, such equivalent models are complex-valued systems and, in this scenario, the conventional passivity constraints are not applicable anymore. Hence, the passivity constraints for scattering parameters and state-space models of baseband equivalent systems are presented, which are essential for time-domain simulations. Three suitable examples demonstrate the feasibility, accuracy, and efficiency of the proposed method.

We demonstrate the use of stochastic collocation to assess the performance of photonic devices under the effect of uncertainty. This approach combines high accuracy and efficiency in analyzing device variability with the ease of implementation of sampling-based methods. Its flexibility makes it suitable to be applied to a large range ofphotonic devices. We compare the stochastic collocation method with a Monte Carlo technique on a numerical analysis of the variability in silicon directional couplers.