Abstract
A major difficulty in applying computational design methods to nanophotonic devices is ensuring that the resulting designs are fabricable. Here, we describe a general inverse design algorithm for nanophotonic devices that directly incorporates fabrication constraints. To demonstrate the capabilities of our method, we designed a spatial-mode demultiplexer, wavelength demultiplexer, and directional coupler. We also designed and experimentally demonstrated a compact, broadband 1 × 3 power splitter on a silicon photonics platform. The splitter has a footprint of only 3.8 × 2.5 μm, and is well within the design rules of a typical silicon photonics process, with a minimum radius of curvature of 100 nm. Averaged over the designed wavelength range of 1400–1700 nm, our splitter has a measured insertion loss of 0.642 ± 0.057 dB and power uniformity of 0.641 ± 0.054 dB.
Highlights
Nanophotonic devices are typically designed by starting with an analytically designed structure, and hand-tuning a few parameters[1]
Building on our previous work[5, 7, 9], we propose an inverse design method for nanophotonic devices that incorporates fabrication constraints
We demonstrate the capabilities of our method by designing a spatial-mode demultiplexer, wavelength demultiplexer, and directional coupler, and experimentally demonstrating an ultra-broadband 1 × 3 power splitter
Summary
Due to the complexity of accurately modelling lithography and etching processes, most attempts to incorporate fabrication constraints into computational nanophotonic design have focused on heuristic methods. We can represent our structure by constructing a continuous function φ(x, y): 2 → over our design region, and letting the boundaries between the materials lie on the level set φ = 0. We evolve our structure, represented by φ, in such a way that we minimize our objective f We can achieve this by adapting gradient descent optimization to our level set representation. As t → ∞, φ converges to a locally optimal structure This approach tends to result in the formation of extremely small features. Curvature limiting will eliminate the formation of most small features, it does not prevent the formation of narrow gaps or bridges We detect these features by applying morphological dilation and erosion operations to the set φ > 0, and checking for changes in topology. A detailed description of the objective function f[ε] and implementation details can be found in the supplementary information
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