Dual-comb spectroscopy, in which two synchronized mode-locked lasers are used as light sources of Fourier-transform spectroscopy, attracts much attention by its high spectral resolution and broad spectral bandwidth. However, there exists a trade-off between the spectral acquisition rate and the signal-to-noise ratio or the spectral bandwidth. Here, we overcome this trade-off by employing a mode-locked laser, whose repetition rate is rapidly modulated, together with a fixed-repetition-rate mode-locked laser. Specifically, we performed characterization of molecular vibrations through time-domain coherent Raman spectroscopy. In this demonstration, a high-spectral acquisition rate of 100,000 spectra/s was achieved with a broad bandwidth of 200 – 1400 cm-1.

Tip-enhanced Raman scattering (TERS) is a promising optical and analytical technique. TERS signals generated by a gap-mode configuration where a tip is coupled with a gold substrate can resolve DNA and RNA molecules at a single-molecule resolution. Through our deposition procedure, the molecules can be stretched, uncoiled, and attached to the substrate by its phosphate groups while exposing its nucleobases to the tip. The proof-of-principle RNA sequencing approach significantly advances a direct RNA sequencing technique without RNA labeling or amplification via reverse transcriptase RT-PCR. The demonstrated technique holds promise for next-generation DNA and RNA sequencing.

High-speed tracking of nano-objects is a gateway to understanding processes at the nanoscale. Here we will present our results on tracking single or ensembles of nano-objects inside optofluidic fibers and on-chip waveguides via elastic light scattering. The nano-objects diffuse inside a channel of a microstructured waveguide and the light scattered by the nano-object is detected transversely via a microscope. We will present the fundamentals of this approach and focus on selected results including 3D tracking in dual-core microstructured fibers and revealing the limits of the approach. We will also present first results on tracking inside nanoprinted on-chip waveguides.

Silicon photonics is a powerful platform, offering efficient mass production of photonic circuitry at high yield and low cost. Silicon as an optical material, however, falls short of certain properties that are indispensable for high-performance optical devices. In this talk, we will give an overview on our research in the field of silicon photonic hybrid (SOH) electro-optic modulators. The SOH concept combines advanced silicon photonic circuitry with highly efficient organic electro-optic cladding materials, obtained through theory-guided molecular design. This approach opens new perspectives for advanced photonic integrated circuits in high-speed optical communications and paves the path towards highly efficient THz signal processing.

New materials that exhibit strong optical nonlinearities at a desired operational frequency are of paramount importance for nonlinear optics. We investigated a refractory metallic/dielectric heterostructured platform, i.e., TiN/Al2O3 epitaxial multilayers, to achieve extreme nonlinearities at NIR frequencies due to the electronic intersubband transitions. This platform has shown χ(2) of 1500 pm/V and n2 of 10-14m2/W at NIR frequencies. Such extreme 2nd and 3rd order nonlinearities have been utilized to achieve efficient second harmonic generation and pulse limiter with total film thickness less than 100nm. The metallic quantum wells could find important applications in nanophotonics, nonlinear optics and meta-.

In this presentation I will give an overview of Rockley Photonics’ real-time, non-invasive biomarker sensing-on-the-wrist integrated photonics technology, and the application of silicon photonics plus AI for smart consumer wearable health sensing applications. I will describe the silicon photonics building blocks needed to make the necessary integrated photonics platform as well as application of AI to provide multi-application, health-related sensing and monitoring applications. I will describe how this platform can provide personalized monitoring of multiple biophysical and biochemical biomarkers, by combining laser-based spectrophotometer capabilities with on-device analytics and a cloud platform which augments stand-alone capabilities with predictive analytics based on biomarker trends.

Development of InP-based U-bend waveguide gain chips for hybrid integration on silicon platform is presented. We utilize Euler bend geometry to ensure small footprint along with low losses. The geometry allows to bring the input and output on the same facet and is used to simplify alignment for lower coupling losses. The interface between bend and straight waveguide is inspected by comparing shallow and deep etched waveguide profiles. The effects of this interface and the bend geometry on the device losses, electric properties and spectrum are reported. Finally, the integration of U-bend gain chips on silicon-on-insulator platform is demonstrated.

Optimization is vital to Engineering, Artificial Intelligence, and to many areas of Science. Mathematically, we usually employ steepest-descent, or other digital algorithms. But, Physics itself, performs optimizations in the normal course of dynamical evolution. Nature provides us with the following optimization principles: 1. The Principle of Least Action; 2. The Variational Principle of Quantum Mechanics; 3. The Principle of Minimum Entropy Generation; 4. The First Mode to Threshold method; 5. The Principle of Least Time; 6. The Adiabatic Evolution method; 7. Quantum Annealing Of these physics principles, “Minimum Entropy Generation” in the form of bistable electrical or optical circuits is particularly adaptable toward offering digital Optimization. For example, we provide the electrical circuit which can address the challenging Ising problem. Since Onsager, 1930, introduced the Principle of Minimum Entropy Generation we call this Onsager Computing.

The widespread use of metamaterials and non-trivial geometries has radically changed the way photonic integrated devices are developed, opening new design possibility and allowing for unprecedented performance. Yet, these devices are often described by a large number of interrelated parameters which cannot be handled manually, requiring innovative design approaches for their effective optimization. In this invited talk, we will discuss the potentiality offered by the combination of machine learning dimensionality reduction and multi-objective optimization for the design of high performance photonic integrated devices.

The optical neural network (ONN) is a promising neuromorphic framework for implementing deep learning tasks thanks to the key features of light, such as high parallelism, low latency, and low power consumption. As the size of deep neural networks (DNNs) continues to grow, so do the training and control difficulties of the corresponding photonic hardware accelerators. Therefore, it is essential to reduce the complexity of ONNs while maintaining accuracy. Here we propose an ONN architecture based on structured neural networks to reduce the optical component utilization as well as the chip footprint. The model complexity of our proposed ONN can be further optimized by incorporating current DNN pruning strategies. Meanwhile, a hardware-aware on-chip training flow is also proposed to improve the learnability, trainability, and robustness of our architecture. Finally, we experimentally demonstrate the reliability of this architecture with a programmable photonic neural chip and benchmarked its performance on multiple datasets.

New photonic and plasmonic concepts have paved the way for novel optoelectronic architectures that are smaller, faster, and provide new functionality. In this talk, I will overview our recent work developing self-powered solar windows, novel photonic energy harvesters, and compact telecom detectors that exploit plasmonic and hot carrier effects. These devices have been enabled by breakthroughs in these fields and have the potential to open new avenues for integrated smart optoelectronics that combine devices into novel systems.

Programmable integrated photonics circuits have evolved in complexity during the last 10 years. As an alternative to custom-design circuits, multipurpose programmable circuits were recently presented with the promise of achieving arbitrary functionality and dynamic system operation. In this talk we review the fundaments of programmable photonics, with special focus on programming methods, performance, and its applications. These devices are called to revolutionize the PIC industry by providing flexible, multi-functional operation with an unprecedented level of customization.

Increasing the refractive index available for optical and nanophotonic systems opens new vistas for optical design, for applications from metalenses to high-quality-factor resonators. In this work, we derive fundamental limits to the refractive index of any material, given only the underlying electron density and either the maximum allowable dispersion or the minimum bandwidth of interest. In the realm of small to modest dispersion, our bounds are closely approached by a wide range of natural materials, showing that nature has already reached a Pareto frontier for refractive index and dispersion. Conversely, for narrow-bandwidth applications, nature does not provide the highly dispersive, high-index materials that our bounds suggest should be possible. We identify metal-based metamaterials that can exhibit small losses and sizeable increases in refractive index over the current best materials.

Image processing has become a critical technology in a variety of science and engineering disciplines. While most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed though it requires a large volume. Meta-optics provide the advantage of thin form factor optics while also allowing complex transfer functions to be employed. In this talk, I will discuss the use of meta-optics for applications in image processing. Specifically, I will discuss meta-optic pre-filters including edge filters as well as filters for identifying higher level spatial features. These meta-optics are designed in conjunction with the digital system and I will discuss how co-design can make the hybrid optical / digital system more tolerant to deterministic and stochastic noise. These analog optical operations can be used to replace, or augment, digital processes for increasing speed while reducing power consumption.

We present electrically switchable nanoantennas whose plasmon resonance can be switched on and off electrically and individually. We demonstrate video-rate switching times, hence overcoming the previous speed issues. In order to validate our concept, we combine our electrically switchable plasmonic nanoantennas in functional metasurfaces. We demonstrate a flat metalens whose focusing ability can be switched on and off electrically. Our system can be operated in transmission as well as in reflection geometry. Our approach represents the breakthrough that will ultimately enable spatial light modulators with densities higher than 1000 lp/mm. This is the crucial element to make possible high-angle variable beam steering, electrically adressable zoom metalenses, and, ultimately, wide-angle holographic videos.