Photonic integrated circuits (PICs) are considered as the way to make photonic systems or subsystems cheap and ubiquitous. PICs still are several orders of magnitude more expensive than their microelectronic counterparts, which has restricted their application to a few niche markets.

Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using “in situ backpropagation,” a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ( > 94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.

Slow light devices such as photonic crystal waveguides (PhCW) and coupled resonator optical waveguides (CROW) have much promise for optical signal processing applications and a number of successful demonstrations underpinning this promise have already been made. Most of these applications are limited by propagation losses, especially for higher group indices. These losses are caused by technological imperfections (“extrinsic loss”) that cause scattering of light from the waveguide mode. The relationship between this loss and the group velocity is complex and until now has not been fully understood. Here, we present a comprehensive explanation of the extrinsic loss mechanisms in PhC waveguides and address some misconceptions surrounding loss and slow light that have arisen in recent years. We develop a theoretical model that accurately describes the loss spectra of PhC waveguides. One of the key insights of the model is that the entire hole contributes coherently to the scattering process, in contrast to previous models that added up the scattering from short sections incoherently. As a result, we have already realised waveguides with significantly lower losses than comparable photonic crystal waveguides as well as achieving propagation losses, in units of loss per unit time (dB/ns) that are even lower than those of state-of-the-art coupled resonator optical waveguides based on silicon photonic wires. The model will enable more advanced designs with further loss reduction within existing technological constraints.

A simple technique for the exact synthesis of selective bandpass filters for wavelength division multiplexing (WDM) systems is presented. The filters consist of direct-coupled ring resonators or cascaded Bragg gratings. The proposed technique permits the calculation of the physical dimensions of the optical structures given the desired frequency response of the filter in terms of bandwidth, free spectral range, out-of-band rejection, and frequency characteristic. The technique, explained in detail for Butterworth and Chebyshev frequency responses, is exact, uses closed-form formulae, and is physically well based. The resulting devices are very compact, have a high out-of-band attenuation, and are suitable for high-density integrated optics. Several filters for applications such as channel add-drop, channel selection, demultiplexing, multichannel filtering, and interleavers are designed, analyzed, and discussed in the paper.