Techniques for Reliability and Robustness in Integrated Electronic and Photonic Systems

Abstract

Reliability and robustness are key concerns in the development of novel electronic and photonic materials, devices, and systems. This thesis presents statistical and machine learning techniques for reliability analysis of heterogeneously-integrated systems, extraction of variations from photonic test structure measurements, making smart decisions about test configurations in the face of time and resource constraints, and robust design of photonic components. To estimate reliability model parameters from lifetime datasets where multiple underlying failure mechanisms are present, a differential evolution framework and a boundconstrained expectation maximization algorithm are developed; both these approaches significantly outperform the gradient-based L-BFGS-B algorithm. New schemes for strategic failure analysis on a subset of the failed units are presented, both for detecting the presence of a second failure mechanism and for improving two-mechanism reliability models. A regression-based protocol is also presented for optimally selecting reliability test conditions to verify physical failure mechanism models. A maximum-likelihood-estimation-based approach is demonstrated for the simultaneous extraction of waveguide index and thickness variations using integrated photonic direction couplers and Mach-Zehnder interferometers. Schemes are proposed for optimal selection of cut-back test structures and for propagation loss estimation with a Bayesian prior distribution for fiber-coupling error. Finally, a robust Bayesian optimization algorithm using a new tunable acquisition function is presented for photonic component design. The methods developed in this thesis are expected to be broadly applicable to a wide variety of electronic and photonic devices and systems.