The explosive growth of data-centric artificial intelligence applications calls for the next generation of optical interconnects for future hyperscale data centers and high-performance computing (HPC) systems. To unleash the full potential of dense wavelength-division multiplexing, we present the design and exploration of a novel transceiver architecture based on silicon photonic micro-resonators featuring a broadband Kerr frequency comb source and fabrication-robust (de-)interleaving structures. In contrast to the traditional single-bus architecture, our architecture de-interleaves the comb onto multiple buses before traversing separate banks of cascaded resonant modulators/filters, effectively doubling the channel spacing with each stage of de-interleaving. With a closed-form free spectral range (FSR) engineering technique guiding the micro-resonator design, the architecture is scalable toward hundreds of parallel channels—spanning much wider than the resonator FSRs—with minimal crosstalk penalty and thermal tuning overhead. This unique architecture, designed with co-packageability in mind, thus enables a multi-Tbps aggregated data rate with moderate per-channel data rates, paving the way for sub-pJ/b ultra-high-bandwidth chip-to-chip connectivity in future data centers and HPC systems.
For integrated silicon photonics to mature as an industry platform, robust methods for measuring and extracting the geometry of fabricated waveguides are needed. Due to the cost and time needed for SEM or AFM imaging, a method of extracting waveguide variability though optical measurements is often preferred. Here, we present a study of regression-based machine learning (ML) techniques that enable such variability extraction while maintaining compatibility with wafer-scale optical measurements. We first explicitly investigate the issue of non-unique effective and group index pairs that can affect the accuracy of regression-based techniques. Training data is then generated by simulating several geometries of wire waveguides in Lumerical’s MODE solver to simulate defects due to process variances. Finally, a representative set of ML regression techniques are tested for their ability to accurately estimate the geometries of said simulated waveguides. To the best of the authors' knowledge, this work represents the first attempt in the literature to i.) explicitly study the effects of non-uniqueness in optical measurement-based metrology and ii) present a model that potentially overcomes said non-uniqueness. This work represents an important step towards the maturing models for process variations in silicon photonic platforms.
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