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Freeform optics aims to expand the toolkit of optical elements by allowing for more complex phase geometries beyond rotational symmetry. Complex, asymmetric curvatures are employed to enhance the performance of optical components while minimizing their weight and size. Unfortunately, these asymmetric forms are often difficult to manufacture at the nanoscale with current technologies. Metasurfaces are planar sub-wavelength structures that can control the phase, amplitude, and polarization of incident light, and can thereby mimic complex geometric curvatures on a flat, wavelength-scale thick surface. We present a methodology for designing analogues of freeform optics using a low contrast dielectric metasurface platform for operation at visible wavelengths. We demonstrate a cubic phase plate with a point spread function exhibiting enhanced depth of field over 300 {mu}m along the optical axis with potential for performing metasurface-based white light imaging, and an Alvarez lens with a tunable focal length range of over 2.5 mm with 100 {mu}m of total mechanical displacement. The adaptation of freeform optics to a sub-wavelength metasurface platform allows for the ultimate miniaturization of optical components and offers a scalable route toward implementing near-arbitrary geometric curvatures in nanophotonics.
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Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations.
Hexagonal boron nitride (hBN)-long-known as a thermally stable ceramic-is now available as atomically smooth, single-crystalline flakes, revolutionizing its use in optoelectronics. For nanophotonics, these flakes offer strong nonlinearities, hyperbolic dispersion, and single-photon emission, providing unique properties for optical and quantum-optical applications. For nanoelectronics, their pristine surfaces, chemical stability, and wide bandgap have made them the key substrate, encapsulant, and gate dielectric for two-dimensional electronic devices. However, while exploring these advantages, researchers have been restricted to flat flakes or those patterned with basic slits and holes, severely limiting advanced architectures. If freely varying flake profiles were possible, the hBN structure would present a powerful design parameter to further manipulate the flow of photons, electrons, and excitons in next-generation devices. Here, we demonstrate freeform nanostructuring of hBN by combining thermal scanning-probe lithography and reactive-ion etching to shape flakes with surprising fidelity. We leverage sub-nanometer height control and high spatial resolution to produce previously unattainable flake structures for a broad range of optoelectronic applications. For photonics, we fabricate microelements and show the straightforward transfer and integration of such elements by placing a spherical hBN microlens between two planar mirrors to obtain a stable, high-quality optical microcavity. We then decrease the patterning length scale to introduce Fourier surfaces for electrons, creating sophisticated, high-resolution landscapes in hBN, offering new possibilities for strain and band-structure engineering. These capabilities can advance the discovery and exploitation of emerging phenomena in hyperbolic metamaterials, polaritonics, twistronics, quantum materials, and 2D optoelectronic devices.
Rare-earth doped nanocrystals are emerging light sources used for many applications in nanotechnology enabled by human ability to control their various optical properties with chemistry and material science. However, one important optical problem -- polarisation of photoluminescence -- remains largely out of control by chemistry methods. Control over photoluminescence polarisation can be gained via coupling of emitters to resonant nanostructures such as optical antennas and metasurfaces. However, the resulting polarization is typically sensitive to position disorder of emitters, which is difficult to mitigate. Recently, new classes of disorder-immune optical systems have been explored within the framework of topological photonics. Here we explore disorder-robust topological arrays of Mie-resonant nanoparticles for polarisation control of photoluminescence of nanocrystals. We demonstrate polarized emission from rare-earth-doped nanocrystals governed by photonic topological edge states supported by zigzag arrays of dielectric resonators. We verify the topological origin of polarised photoluminescence by comparing emission from nanoparticles coupled to topologically trivial and nontrivial arrays of nanoresonators.
Reflective imaging systems form an important part of photonic devices such as spectrometers, telescopes, augmented and virtual reality headsets or lithography platforms. Reflective optics provide unparalleled spectral performance and can be used to reduce overall volume and weight. So far, most reflective designs have focused on two or three reflections, while four-reflection freeform designs can deliver a higher light throughput (faster F-number) as well as a larger field-of-view (FOV). However, advanced optical design strategies for four-reflection freeform systems have been rarely reported in literature. This is due to the increased complexity in solution space but also the fact that additional mirrors hinder a cost-effective realization (manufacture, alignment, etc.). Recently, we have proposed a novel design method to directly calculate the freeform surface coefficients while merely knowing the mirror positions and tilts. Consequently, this method allows laymen with basic optical design knowledge to calculate first time right freeform imaging systems in a matter of minutes. This contrasts with most common freeform design processes, which requires considerable experience, intuition or guesswork. Firstly, we demonstrate the effectiveness of the proposed method for a four-mirror high-throughput telescope with 250mm-focal-length, F/2.5 and a wide rectangular FOV of 8.5{deg} x 25.5{deg}. In a subsequent step, we propose an effective three-mirror but four-reflection imaging system, which consists of two freeform mirrors and one double-reflection spherical mirror. Compared with common three-mirror and three-reflection imagers, our novel multi-reflection system shows unprecedented possibilities for an economic implementation while drastically reducing the overall volume.
Recent advancements in computational inverse design have begun to reshape the landscape of structures and techniques available to nanophotonics. Here, we outline a cross section of key developments at the intersection of these two fields: moving from a recap of foundational results to motivation of emerging applications in nonlinear, topological, near-field and on-chip optics.
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