This paper presents a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (SPINS). Borrowing the concept of computational graphs, SPINS is a design framework that emphasizes flexibility and reproducible results. By factoring the inverse design process into components that can be swapped out for one another, SPINS enables inverse design practitioners to easily explore different design methodologies. Here, we present the mathematical and architectural details on how to achieve these goals, using the inverse design of a wavelength demultiplexer as a primary example. Using inverse design effectively requires understanding the “control knobs” available to the designer, and, to that end, we also discuss practical considerations and heuristics for effective use of inverse design. In particular, by running inverse design on hundreds of designs of 3D wavelength demultiplexers, this paper explores the landscape of local minima, which leads to insights on the choice of initial conditions.
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