Efficient variability analysis of photonic circuits by stochastic parametric building blocks

This paper presents a method to build stochastic parametric building blocks to be used in photonic process design kits. The building blocks are based on parametric macro-models computed by means of the generalized Polynomial Chaos Expansion technique. These macro-models can be built upfront and stored in process design kits. Being parametric, they do not have to be recalculated if the value of their design or statistical parameters change. It is shown that a single deterministic simulation performed with a classical circuit simulator is sufficient to perform the statistical analysis of any arbitrary photonic circuit realized combining these building blocks with different parameters, without the need of time-consuming Monte Carlo approach. Relevant numerical examples are used to demonstrate that the proposed macro-models are truly parametric, inherently stochastic and have greater simulation efficiency compared to Monte Carlo