Silicon Photonics technology is rapidly maturing as a platform for larger‐scale photonic
circuits. As a result, the associated design methodologies are also evolving from component …

This work develops an enhanced Monte Carlo (MC) simulation methodology to predict the
impacts of layout-dependent correlated manufacturing variations on the performance of …

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and
nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo …

Silicon photonic devices are very sensitive to process variation, and it is important for circuit
designers that they can predict the effect of this variability during the design phase, and …

Microwave photonics aims to overcome the limitations of radiofrequency devices and
systems by leveraging the unique properties of optics in terms of low loss and power …

The implementation of nonlinear activation functions is one of the key challenges that optical
neural networks face. To the date, different approaches have been proposed, including …

We present a simulation framework for evaluating the effect of location-dependent variability
in photonic integrated circuits. The framework combines a fast circuit simulator with circuit …

We experimentally demonstrate extraction of silicon waveguide geometry with
subnanometer accuracy using optical measurements. Effective and group indices of silicon …

Fabrication process variations are a major source of yield degradation in the nanoscale
design of integrated circuits (ICs), microelectromechanical systems (MEMSs), and photonic …

This paper introduces an innovative data-driven approach to uncertainty quantification (UQ)
in complex engineering designs based on polynomial chaos expansion (PCE) and least …