Photonic flatband resonances for free-electron radiation.

Flatbands have become a cornerstone of contemporary condensed-matter physics and photonics. In electronics, flatbands entail comparable energy bandwidth and Coulomb interaction, leading to correlated phenomena such as the fractional quantum Hall effect and recently those in magic-angle systems. In photonics, they enable properties including slow light1 and lasing2. Notably, flatbands support supercollimation-diffractionless wavepacket propagation-in both systems3,4. Despite these intense parallel efforts, flatbands have never been shown to affect the core interaction between free electrons and photons. Their interaction, pivotal for free-electron lasers5, microscopy and spectroscopy6,7, and particle accelerators8,9, is, in fact, limited by a dimensionality mismatch between localized electrons and extended photons. Here we reveal theoretically that photonic flatbands can overcome this mismatch and thus remarkably boost their interaction. We design flatband resonances in a silicon-on-insulator photonic crystal slab to control and enhance the associated free-electron radiation by tuning their trajectory and velocity. We observe signatures of flatband enhancement, recording a two-order increase from the conventional diffraction-enabled Smith-Purcell radiation. The enhancement enables polarization shaping of free-electron radiation and characterization of photonic bands through electron-beam measurements. Our results support the use of flatbands as test beds for strong light-electron interaction, particularly relevant for efficient and compact free-electron light sources and accelerators.

Transcending shift-invariance in the paraxial regime via end-to-end inverse design of freeform nanophotonics.

Traditional optical elements and conventional metasurfaces obey shift-invariance in the paraxial regime. For imaging systems obeying paraxial shift-invariance, a small shift in input angle causes a corresponding shift in the sensor image. Shift-invariance has deep implications for the design and functionality of optical devices, such as the necessity of free space between components (as in compound objectives made of several curved surfaces). We present a method for nanophotonic inverse design of compact imaging systems whose resolution is not constrained by paraxial shift-invariance. Our method is end-to-end, in that it integrates density-based full-Maxwell topology optimization with a fully iterative elastic-net reconstruction algorithm. By the design of nanophotonic structures that scatter light in a non-shift-invariant manner, our optimized nanophotonic imaging system overcomes the limitations of paraxial shift-invariance, achieving accurate, noise-robust image reconstruction beyond shift-invariant resolution.

End-to-end metasurface inverse design for single-shot multi-channel imaging.

We introduce end-to-end inverse design for multi-channel imaging, in which a nanophotonic frontend is optimized in conjunction with an image-processing backend to extract depth, spectral and polarization channels from a single monochrome image. Unlike diffractive optics, we show that subwavelength-scale "metasurface" designs can easily distinguish similar wavelength and polarization inputs. The proposed technique integrates a single-layer metasurface frontend with an efficient Tikhonov reconstruction backend, without any additional optics except a grayscale sensor. Our method yields multi-channel imaging by spontaneous demultiplexing: the metaoptics front-end separates different channels into distinct spatial domains whose locations on the sensor are optimally discovered by the inverse-design algorithm. We present large-area metasurface designs, compatible with standard lithography, for multi-spectral imaging, depth-spectral imaging, and "all-in-one" spectro-polarimetric-depth imaging with robust reconstruction performance (≲ 10% error with 1% detector noise). In contrast to neural networks, our framework is physically interpretable and does not require large training sets. It can be used to reconstruct arbitrary three-dimensional scenes with full multi-wavelength spectra and polarization textures.

Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Fullwave Maxwell inverse design of axisymmetric, tunable, and multi-scale multi-wavelength metalenses.

We demonstrate new axisymmetric inverse-design techniques that can solve problems radically different from traditional lenses, including reconfigurable lenses (that shift a multi-frequency focal spot in response to refractive-index changes) and widely separated multi-wavelength lenses (λ = 1 µm and 10 µm). We also present experimental validation for an axisymmetric inverse-designed monochrome lens in the near-infrared fabricated via two-photon polymerization. Axisymmetry allows fullwave Maxwell solvers to be scaled up to structures hundreds or even thousands of wavelengths in diameter before requiring domain-decomposition approximations, while multilayer topology optimization with ∼105 degrees of freedom can tackle challenging design problems even when restricted to axisymmetric structures.

Single-Layer Metasurface with Controllable Multiwavelength Functions.

In this paper, we report dispersion-engineered metasurfaces with distinct functionalities controlled by wavelength. Unlike previous approaches based on spatial multiplexing or vertical stacking of metasurfaces, we utilize a single phase profile with wavelength dependence encoded in the phase shifters' dispersion. We designed and fabricated a multiwavelength achromatic metalens (MAM) with achromatic focusing for blue (B), green (G), yellow (Y), and red (R) light and two wavelength-controlled beam generators (WCBG): one focuses light with orbital angular momentum (OAM) states ( l = 0,1,2) corresponding to three primary colors; the other produces ordinary focal spots ( l = 0) for red and green light, while generating a vortex beam ( l = 1) in the blue. A full color (RGB) hologram is also demonstrated in simulation. Our approach opens a path to applications ranging from near-eye displays and holography to compact multiwavelength beam generation.

Substrate aberration and correction for meta-lens imaging: an analytical approach.

Meta-lenses based on flat optics enabled a fundamental shift in lens production-providing an easier manufacturing process with an increase in lens profile precision and a reduction in size and weight. Here we present an analytical approach to correct spherical aberrations caused by light propagation through the substrate by adding a substrate-corrected phase profile, which differs from the original hyperbolic one. A meta-lens encoding the new phase profile would yield diffraction-limited focusing and an increase of up to 0.3 of its numerical aperture without changing the radius or focal length. In tightly focused laser spot applications such as direct laser lithography and laser printing, a substrate-corrected meta-lens can reduce the spatial footprint of the meta-lens.

Learning photons go backward.

Efficient learning algorithms are implemented in a silicon photonic neural network chip.

Biasing the quantum vacuum to control macroscopic probability distributions.

Quantum field theory suggests that electromagnetic fields naturally fluctuate, and these fluctuations can be harnessed as a source of perfect randomness. Many potential applications of randomness rely on controllable probability distributions. We show that vacuum-level bias fields injected into multistable optical systems enable a controllable source of quantum randomness, and we demonstrated this concept in an optical parametric oscillator (OPO). By injecting bias pulses with less than one photon on average, we controlled the probabilities of the two possible OPO output states. The potential of our approach for sensing sub-photon-level fields was demonstrated by reconstructing the temporal shape of fields below the single-photon level. Our results provide a platform to study quantum dynamics in nonlinear driven-dissipative systems and point toward applications in probabilistic computing and weak field sensing.

A framework for scintillation in nanophotonics.

Bombardment of materials by high-energy particles often leads to light emission in a process known as scintillation. Scintillation has widespread applications in medical imaging, x-ray nondestructive inspection, electron microscopy, and high-energy particle detectors. Most research focuses on finding materials with brighter, faster, and more controlled scintillation. We developed a unified theory of nanophotonic scintillators that accounts for the key aspects of scintillation: energy loss by high-energy particles, and light emission by non-equilibrium electrons in nanostructured optical systems. We then devised an approach based on integrating nanophotonic structures into scintillators to enhance their emission, obtaining nearly an order-of-magnitude enhancement in both electron-induced and x-ray-induced scintillation. Our framework should enable the development of a new class of brighter, faster, and higher-resolution scintillators with tailored and optimized performance.

Monochromatic X-ray Source Based on Scattering from a Magnetic Nanoundulator.

We present a novel design for an ultracompact, passive light source capable of generating ultraviolet and X-ray radiation, based on the interaction of free electrons with the magnetic near-field of a ferromagnet. Our design is motivated by recent advances in the fabrication of nanostructures, which allow the confinement of large magnetic fields at the surface of ferromagnetic nanogratings. Using ab initio simulations and a complementary analytical theory, we show that highly directional, tunable, monochromatic radiation at high frequencies could be produced from relatively low-energy electrons within a tabletop design. The output frequency is tunable in the extreme ultraviolet to hard X-ray range via electron kinetic energies from 1 keV to 5 MeV and nanograting periods from 1 µm to 5 nm. The proposed radiation source can achieve the tunability and monochromaticity of current free-electron-driven sources (free-electron lasers, synchrotrons, and laser-driven undulators), yet with a significantly reduced scale, cost, and complexity. Our design could help realize the next generation of tabletop or on-chip X-ray sources.

Heuristic recurrent algorithms for photonic Ising machines.

The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.

Towards integrated tunable all-silicon free-electron light sources.

Extracting light from silicon is a longstanding challenge in modern engineering and physics. While silicon has underpinned the past 70 years of electronics advancement, a facile tunable and efficient silicon-based light source remains elusive. Here, we experimentally demonstrate the generation of tunable radiation from a one-dimensional, all-silicon nanograting. Light is generated by the spontaneous emission from the interaction of these nanogratings with low-energy free electrons (2-20 keV) and is recorded in the wavelength range of 800-1600 nm, which includes the silicon transparency window. Tunable free-electron-based light generation from nanoscale silicon gratings with efficiencies approaching those from metallic gratings is demonstrated. We theoretically investigate the feasibility of a scalable, compact, all-silicon tunable light source comprised of a silicon Field Emitter Array integrated with a silicon nanograting that emits at telecommunication wavelengths. Our results reveal the prospects of a CMOS-compatible electrically-pumped silicon light source for possible applications in the mid-infrared and telecommunication wavelengths.