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Register a new userMachine learning assisted global optimization of photonic devices
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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.
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With the emergence of new photonic and plasmonic materials with optimized properties as well as advanced nanofabrication techniques, nanophotonic devices are now capable of providing solutions to global challenges in energy conversion, information technologies, chemical/biological sensing, space exploration, quantum computing, and secure communication. Addressing grand challenges poses inherently complex, multi-disciplinary problems with a manifold of stringent constraints in conjunction with the required systems performance. Conventional optimization techniques have long been utilized as powerful tools to address multi-constrained design tasks. One example is so-called topology optimization that has emerged as a highly successful architect for the advanced design of non-intuitive photonic structures. Despite many advantages, this technique requires substantial computational resources and thus has very limited applicability to highly constrained optimization problems within high-dimensions parametric space. In our approach, we merge the topology optimization method with machine learning algorithms such as adversarial autoencoders and show substantial improvement of the optimization process by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.
Opto-mechanical interactions in planar photonic integrated circuits draw great interest in basic research and applications. However, opto-mechanics is practically absent in the most technologically significant photonics platform: silicon on insulator. Previous demonstrations required the under-etching and suspension of silicon structures. Here we present surface acoustic wave-photonic devices in silicon on insulator, up to 8 GHz frequency. Surface waves are launched through absorption of modulated pump light in metallic gratings and thermoelastic expansion. The surface waves are detected through photo-elastic modulation of an optical probe in standard race-track resonators. Devices do not involve piezo-electric actuation, suspension of waveguides or hybrid material integration. Wavelength conversion of incident microwave signals and acoustic true time delays up to 40 ns are demonstrated on-chip. Lastly, discrete-time microwave-photonic filters with up to six taps and 20 MHz wide passbands are realized using acoustic delays. The concept is suitable for integrated microwave-photonics signal processing
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.
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.
One of the main characteristics of optical imaging systems is the spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $sqrt{n}$ improvement in the spatial resolution by measuring the n-th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.
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