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Register a new userMeasurements and modeling of atomic-scale sidewall roughness and losses in integrated photonic devices
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Atomic-level imperfections play an increasingly critical role in nanophotonic device performance. However, it remains challenging to accurately characterize the sidewall roughness with sub-nanometer resolution and directly correlate this roughness with device performance. We have developed a method that allows us to measure the sidewall roughness of waveguides made of any material (including dielectrics) using the high resolution of atomic force microscopy. We illustrate this method by measuring state-of-the-art photonic devices made of silicon nitride. We compare the roughness of devices fabricated using both DUV photo-lithography and electron-beam lithography for two different etch processes. To correlate roughness with device performance we describe what we call a new Payne-Lacey Bending model, which adds a correction factor to the widely used Payne-Lacey model so that losses in resonators and waveguides with bends can be accurately predicted given the sidewall roughness, waveguide width and bending radii. Having a better way to measure roughness and use it to predict device performance can allow researchers and engineers to optimize fabrication for state-of-the-art photonics using many materials.
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Optical unitary converter (OUC) that can convert a set of N mutually orthogonal optical modes into another set of arbitrary N orthogonal modes is expected to be the key device in diverse applications, including the optical communication, deep learning, and quantum computing. While various types of OUC have been demonstrated on photonic integration platforms, its sensitivity against a slight deviation in the waveguide dimension has been the crucial issue in scaling N. Here, we demonstrate that an OUC based on the concept of multi-plane light conversion (MPLC) shows outstanding robustness against waveguide deviations. Moreover, it becomes more and more insensitive to fabrication errors as we increase N, which is in clear contrast to the conventional OUC architecture, composed of 2 $times$ 2 Mach-Zehnder interferometers. The physical origin behind this unique robustness and scalability is studied by considering a generalized OUC configuration. As a result, we reveal that the number of coupled modes in each stage plays an essential role in determining the sensitivity of the entire OUC. The maximal robustness is attained when all-to-all-coupled interferometers are employed, which are naturally implemented in MPLC-OUC.
Waves that are perfectly confined in the continuous spectrum of radiating waves without interaction with them are known as bound states in the continuum (BICs). Despite recent discoveries of BICs in nanophotonics, full routing and control of BICs are yet to be explored. Here, we experimentally demonstrate BICs in a fundamentally new photonic architecture by patterning a low-refractive-index material on a high-refractive-index substrate, where dissipation to the substrate continuum is eliminated by engineering the geometric parameters. Pivotal BIC-based photonic components are demonstrated, including waveguides, microcavities, directional couplers, and modulators. Therefore, this work presents the critical step of photonic integrated circuits in the continuum, and enables the exploration of new single-crystal materials on an integrated photonic platform without the fabrication challenges of patterning the single-crystal materials. The demonstrated lithium niobate platform will facilitate development of functional photonic integrated circuits for optical communications, nonlinear optics at the single photon level as well as scalable photonic quantum information processors.
Optical beamforming networks (OBFNs) based on optical true time delay lines (OTTDLs) are well-known as the promising candidate to solve the bandwidth limitation of traditional electronic phased array antennas (PAAs) due to beam squinting. Here we report the first monolithic 1x8 microwave photonic beamformer based on switchable OTTDLs on the silicon-on-insulator platform. The chip consists of a modulator, an eight-channel OBFN, and 8 photodetectors, which includes hundreds of active and passive components in total. It has a wide operating bandwidth from 8 to 18 GHz, which is almost two orders larger than that of electronic PAAs. The beam can be steered to 31 distinguishable angles in the range of -75.51{deg} to 75.64{deg} based on the beam pattern calculation with the measured RF response. The response time for beam steering is 56 {mu}s. These results represent a significant step towards the realization of integrated microwave photonic beamformers that can satisfy compact size and low power consumption requirements for the future radar and wireless communication systems.
Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable, by design effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or meta-atoms. Such approach can not provide the global solution to a complex optimization problem where both meta-atoms shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine-learning-assisted global optimization framework for photonic meta-devices design. We demonstrate that using an adversarial autoencoder coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the meta-devices configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of adversarial autoencoder based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design intuition by revealing the underlying physics of the optical performance of meta-devices with complex topologies and material compositions.
Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks, which are encountered in fundamental areas and real-world practical problems including logistics, social networks, and cryptography. Physical machines have recently been proposed and implemented as an alternative to conventional exact and heuristic solvers for the Ising problem, one such optimization task that requires finding the ground state spin configuration of an arbitrary Ising graph. However, these physical approaches usually suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS) capable of converging to the ground state of various 4-spin graphs with high probability. The INPRIS exploits experimental physical noise as a resource to speed up the ground state search. By injecting additional extrinsic noise during the algorithm iterations, the INPRIS explores larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem, and could thus be implemented with optoelectronic architectures that enable GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), our work paves a way for orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.
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