Inference in artificial intelligence with deep optics and photonics

Polarisation optics for biomedical and clinical applications: a review

Many polarisation techniques have been harnessed for decades in biological and clinical research, each based upon measurement of the vectorial properties of light or the vectorial transformations imposed on light by objects. Various advanced vector measurement/sensing techniques, physical interpretation methods, and approaches to analyse biomedically relevant information have been developed and harnessed. In this review, we focus mainly on summarising methodologies and applications related to tissue polarimetry, with an emphasis on the adoption of the Stokes-Mueller formalism. Several recent breakthroughs, development trends, and potential multimodal uses in conjunction with other techniques are also presented. The primary goal of the review is to give the reader a general overview in the use of vectorial information that can be obtained by polarisation optics for applications in biomedical and clinical research.

Ultra-dense perfect optical orbital angular momentum multiplexed holography

Optical orbital angular momentum (OAM) has been recently implemented in holography technologies as an independent degree of freedom for boosting information capacity. However, the holography capacity and fidelity suffer from the limited space-bandwidth product (SBP) and the channel crosstalk, albeit the OAM mode set exploited as multiplexing channels is theoretically unbounded. Here, we propose the ultra-dense perfect OAM holography, in which the OAM modes are discriminated both radially and angularly. As such, the perfect OAM mode set constructs the two-dimensional spatial division multiplexed holography (conventional OAM holography is 1D). The extending degree of freedom enhances the holography capacity and fidelity. We have demonstrated an ultra-fine fractional OAM holography with the topological charge resolution of 0.01. A 20-digit OAM-encoded holography encryption has also been exhibited. It harnesses only five angular OAM topological charges ranging from -16 to +16. The SBP efficiency is about 20 times larger than the conventional phase-only OAM holography. This work paves the way to compact, high-security and high-capacity holography.

Fossil Energy in the Framework of Sustainable Development: Analysis of Prospects and Development of Forecast Scenarios

In the next 20 years, the fossil energy must become a guarantor of the sustainable development of the energy sector for future generations. Significant threats represent hurdles in this transition. This study identified current global trends in the energy sector and the prospects for the development of energy until 2035. The importance of risk assessment in scenario forecasting based on expert judgments was proven. Three contrasting scenarios, #StayHome, #StayAlone, and #StayEffective, for the development of fossil energy, all based on comprehensive analysis of global risks by expert survey and factor analysis, were developed. It was concluded that fossil energy is mandatory with integration of advanced technologies at every stage of the production of traditional energy and of renewable energy as an integral part of the modern energy sector. Based on the results of the study, nine ambitious programs for the development of sustainable energy are presented. They require the creation and the utilization of a single interactive digital platform adapted to this purpose. It is a passport mandatory for the flexible interaction of energy production, its transmission, and its consumption in the perspective of having a future sustainable, reliable, and secured energy sector.

Materials for emergent silicon-integrated optical computing

Progress in computing architectures is approaching a paradigm shift: traditional computing based on digital complementary metal-oxide semiconductor technology is nearing physical limits in terms of miniaturization, speed, and, especially, power consumption. Consequently, alternative approaches are under investigation. One of the most promising is based on a "brain-like" or neuromorphic computation scheme. Another approach is quantum computing using photons. Both of these approaches can be realized using silicon photonics, and at the heart of both technologies is an efficient, ultra-low power broad band optical modulator. As silicon modulators suffer from relatively high power consumption, materials other than silicon itself have to be considered for the modulator. In this Perspective, we present our view on such materials. We focus on oxides showing a strong linear electro-optic effect that can also be integrated with Si, thus capitalizing on new materials to enable the devices and circuit architectures that exploit shifting computational machine learning paradigms, while leveraging current manufacturing infrastructure. This is expected to result in a new generation of computers that consume less power and possess a larger bandwidth.

Waveguide-lattice-based architecture for multichannel optical transformations

We consider waveguide lattices as the architecture to implement a wide range of multiport transformations. In this architecture, a particular transfer matrix is obtained by setting step-wise profiles of propagation constants experienced by a field evolving in a lattice. To investigate the capabilities of this architecture, we numerically study the implementation of random transfer matrices as well as several notable cases, such as the discrete Fourier transform, the Hadamard, and permutation matrices. We show that waveguide lattice schemes are more compact than their traditional lumped-parameter counterparts, thus the proposed architecture may be beneficial for photonic information processing systems of the future.

Scalable optical learning operator

Today's heavy machine learning tasks are fueled by large datasets. Computing is performed with power-hungry processors whose performance is ultimately limited by the data transfer to and from memory. Optics is a powerful means of communicating and processing information, and there is currently intense interest in optical information processing for realizing high-speed computations. Here we present and experimentally demonstrate an optical computing framework called scalable optical learning operator, which is based on spatiotemporal effects in multimode fibers for a range of learning tasks including classifying COVID-19 X-ray lung images, speech recognition and predicting age from images of faces. The presented framework addresses the energy scaling problem of existing systems without compromising speed. We leverage simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. We numerically and experimentally show the ability of the method to execute several different tasks with accuracy comparable with a digital implementation.

Multiple-view D2NNs array: realizing robust 3D object recognition

As an optical-based classifier of the physical neural network, the independent diffractive deep neural network (D²NN) can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view D²NNs array (MDA) scheme that provides a significant inference improvement compared with individual D²NN or Res-D²NN by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer. Furthermore, a robust multiple-view D²NNs array (r-MDA) framework is demonstrated to resist the redundant spatial features of invalid lightfields due to severe optical disturbances.

Quo Vadis, Metasurfaces?

The full manipulation of intrinsic properties of electromagnetic waves has become the central target in various modern optical technologies. Optical metasurfaces have been suggested for a complete control of light-matter interaction with subwavelength structures, and they have been explored widely in the past decade for creating next-generation multifunctional flat-optics devices. The current studies of metasurfaces have reached a mature stage where common materials, basic optical physics, and conventional engineering tools have been explored extensively for various applications such as light bending, metalenses, metaholograms, and many others. A natural question is where the future research on metasurfaces will be going: Quo vadis, metasurfaces? In this Mini Review, we provide perspectives on the future developments of optical metasurfaces. Specifically, we highlight recent progresses on hybrid metasurfaces employing low-dimensional materials and discuss biomedical, computational, and quantum applications of metasurfaces, followed by discussions of challenges and foreseeing the future of metasurface physics and engineering.

Phase-stable repetition rate multiplication of dual-comb spectroscopy based on a cascaded Mach-Zehnder interferometer

In this Letter, we demonstrate a passive all-fiber pulse delay method for repetition rate multiplication of dual-comb spectroscopy. By combining a cascaded Mach-Zehnder interferometer and digital error correction, a mode-resolved spectrum with improved acquisition speed and sensitivity can be obtained. This technique has the strengths of compact, broadband, high energetic efficiency, and low complexity. Due to the use of an adaptive post-processing algorithm, sophisticated closed-loop feedback electronics are not required, which provides a simple and effective scheme to break through the physical limitation of the repetition frequency of the frequency comb for phase-stable dual-comb applications.

Robust and efficient single-pixel image classification with nonlinear optics

We present a hybrid image classifier by feature-sensitive image upconversion, single pixel photodetection, and deep learning, aiming at fast processing of high-resolution images. It uses partial Fourier transform to extract the images' signature features in both the original and Fourier domains, thereby significantly increasing the classification accuracy and robustness. Tested on the Modified National Institute of Standards and Technology handwritten digit images and verified by simulation, it boosts accuracy from 81.25% (by Fourier-domain processing) to 99.23%, and achieves 83% accuracy for highly contaminated images whose signal-to-noise ratio is only -17dB. Our approach could prove useful for fast lidar data processing, high-resolution image recognition, occluded target identification, and atmosphere monitoring.

Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3, sensing4, medical diagnostics5 and computing6,7.