Low loss, high contrast optical waveguides based on CMOS compatible LPCVD processing

A new class of integrated optical waveguide structures is presented, based on low cost CMOS compatible LPCVD processing. This technology allows for medium and high index contrast waveguides with very low channel attenuation. The geometry is basically formed by a rectangular cross-section silicon nitride $(Si_{3}N_{4})$ filled with and encapsulated by silicon dioxide $(SiO_{2})$. The birefringence and minimal bend radius of the waveguide is completely controlled by the geometry of the waveguide layer structures. Experiments on typical geometries will be presented, showing excellent characteristics (channel attenuation ≤0.06 dB/cm, IL ≤0.6 dB, PDL ≤0.2 dB, Bg «1 x $10^{-3}$, bend radius ≤500 μm)

Scalable low-latency optical phase sensor array

Optical phase measurement is critical for many applications, and traditional approaches often suffer from mechanical instability, temporal latency, and computational complexity. In this paper, we describe compact phase sensor arrays based on integrated photonics, which enable accurate and scalable reference-free phase sensing in a few measurement steps. This is achieved by connecting multiple two-port phase sensors into a graph to measure relative phases between neighboring and distant spatial locations. We propose an efficient post-processing algorithm, as well as circuit design rules to reduce random and biased error accumulations. We demonstrate the effectiveness of our system in both simulations and experiments with photonics integrated circuits. The proposed system measures the optical phase directly without the need for external references or spatial light modulators, thus providing significant benefits for applications including microscope imaging and optical phased arrays

A tuning method for photonic integrated circuits in presence of thermal, cross talk

We present a novel method to perform tuning and locking of photonic integrated circuits (PICs) in presence of thermal cross-talk. The unwanted phase coupling among different elements of the circuit is canceled via exploiting the eigen-vectors of the thermally coupled system. The effectiveness of the proposed method is proved on PIC architectures, based on microring resonators (MRRs) and Mach-Zehnder interferometers (MZIs)

Establishing Multiple Chip-to-Chip Orthogonal Free-Space Optical Channels using Programmable Silicon Photonics Meshes

Two silicon photonics programmable meshes of Mach-Zehnder interferometers are used to automatically establish chip-to-chip orthogonal free-space communication links. Optimum channels with mutual isolation of more than 30dB are found even in case of a misaligned link or in presence of an obstacle in the path

Multimode Free Space Optical Link Enabled by SiP Integrated Meshes

A silicon photonic mesh of tuneable Mach-Zehnder Interferometers (MZIs) is employed to receive two spatially-overlapped Hermite-Gaussian beams modulated at 10 Gbit/s, sharing the same wavelength and state of polarization. The mesh automatically self-configures, separating and sorting the two beams out without any excess loss

Separating arbitrary free-space beams with an integrated photonic processor

Free-space optics naturally offers multiple-channel communications and sensing exploitable in many applications. The different optical beams will, however, generally be overlapping at the receiver, and, especially with atmospheric turbulence or other scattering or aberrations, the arriving beam shapes may not even be known in advance. We show that such beams can be still separated in the optical domain, and simultaneously detected with negligible cross-talk, even if they share the same wavelength and polarization, and even with unknown arriving beam shapes. The kernel of the adaptive multibeam receiver presented in this work is a programmable integrated photonic processor that is coupled to free-space beams through a two-dimensional array of optical antennas. We demonstrate separation of beam pairs arriving from different directions, with overlapping spatial modes in the same direction, and even with mixing between the beams deliberately added in the path. With the circuit’s optical bandwidth of more than 40 nm, this approach offers an enabling technology for the evolution of FSO from single-beam to multibeam space-division multiplexed systems in a perturbed environment, which has been a game-changing transition in fiber-optic systems

Experimental evaluation of digitally verifiable photonic computing for blockchain and cryptocurrency

As blockchain technology and cryptocurrency become increasingly mainstream, photonic computing has emerged as an efficient hardware platform that reduces ever-increasing energy costs required to verify transactions in decentralized cryptonetworks. To reduce sensitivity of these verifications to photonic hardware error, we propose and experimentally demonstrate a cryptographic scheme, LightHash, that implements robust, low-bit precision matrix multiplication in programmable silicon photonic networks. We demonstrate an error mitigation scheme to reduce error by averaging computation across circuits, and simulate energy-efficiency-error trade-offs for large circuit sizes. We conclude that our error-resistant and efficient hardware solution can potentially generate a new market for decentralized photonic blockchain

Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks

Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most efficient and well-entrenched method to train such networks is backpropagation, or reverse-mode automatic differentiation. To counter an exponentially increasing energy budget in the artificial intelligence sector, there has been recent interest in analog implementations of neural networks, specifically nanophotonic neural networks for which no analog backpropagation demonstration exists. We design mass-manufacturable silicon photonic neural networks that alternately cascade our custom designed "photonic mesh" accelerator with digitally implemented nonlinearities. These reconfigurable photonic meshes program computationally intensive arbitrary matrix multiplication by setting physical voltages that tune the interference of optically encoded input data propagating through integrated Mach-Zehnder interferometer networks. Here, using our packaged photonic chip, we demonstrate in situ backpropagation for the first time to solve classification tasks and evaluate a new protocol to keep the entire gradient measurement and update of physical device voltages in the analog domain, improving on past theoretical proposals. Our method is made possible by introducing three changes to typical photonic meshes: (1) measurements at optical "grating tap" monitors, (2) bidirectional optical signal propagation automated by fiber switch, and (3) universal generation and readout of optical amplitude and phase. After training, our classification achieves accuracies similar to digital equivalents even in presence of systematic error. Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.Comment: 23 pages, 10 figure