Polygon search algorithm for ultra-compact multifunctional integrated photonics design

Ultra-compact multifunctional integrated photonic modules have great practical significance to photonic integrated circuits (PICs).However,the design effect and efficiency of the existing mainstream inverse design algorithms are incompetent when designing these modules.We analyze their shortcomings in this task,and propose a new,to our knowledge,inverse design algorithm named polygon search (PS) algorithm to address these problems.We utilize the PS algorithm to design an integrated dual-channel mode-conversion-crossing waveguide module.This module integrates three functions:interconversion between TE_(0) and TE_(1),interconversion between TE_(0) and TE_(2),and channel crossing within only a 4μm×4μm footprint,and its performance is verified by experimental testing.It not only greatly reduces the total footprint of many PICs but also greatly improves their fabricating robustness.Furthermore,we propose a PS-designed mode mixer and a PS-designed bending waveguide,and connect them with the integrated modules to form a four-channel crossing-mode-division-multiplexing system.This system can provide multiple modes on the basis of channel crossing and transmit the output signal in the same direction in parallel within a single output waveguide,which significantly increases the communication bandwidth and decreases the footprint of PICs.At last,we demonstrate the effect and efficiency advantages of the PS algorithm over several mainstream inverse design algorithms by a comprehensive contrast experiment and explain these advantages in theory from several perspectives.

Optical trapping-enhanced probes designed by a deep learning approach

Realizing optical trapping enhancement is crucial in biomedicine,fundamental physics,and precision measurement.Taking the metamaterials with artificially engineered permittivity as photonic force probes in optical tweezers will offer unprecedented opportunities for optical trap enhancement.However,it usually involves multi-parameter optimization and requires lengthy calculations;thereby few studies remain despite decades of research on optical tweezers.Here,we introduce a deep learning(DL)model to attack this problem.The DL model can efficiently predict the maximum axial optical stiffness of Si∕Si_(3)N_(4)(SSN)multilayer metamaterial nanoparticles and reduce the design duration by about one order of magnitude.We experimentally demonstrate that the designed SSN nanoparticles show more than twofold and fivefold improvement in the lateral(k_(x)and k_(y))and the axial(k_(z))optical trap stiffness on the high refractive index amorphous TiO_(2)microsphere.Incorporating the DL model in optical manipulation systems will expedite the design and optimization processes,providing a means for developing various photonic force probes with specialized functional behaviors.

Topological multipolar corner state in a supercell metasurface and its interplay with two-dimensional materials

Second-order topological insulators(SOTIs) have recently attracted much attention due to their capability to support lower-dimensional topological states, namely, the corner states. Here, we demonstrate that properly designed supercell metasurfaces can support photonic corner states, meanwhile further serving as an ideal platform for the implementations of topological polaritons and dynamically reconfigurable corner states by assembling two-dimensional materials. Such metasurfaces consist of an array of finite-sized SOTIs mimicking the twodimensional Su–Schrieffer–Heeger model. We reveal that the topological transition happens in unit cells without the bandgap, and nondegenerate multipolar corner states emerge in the supercell metasurface due to the inter-and intrasupercell coupling effects. Especially since these corner states are above the light line of the metasurface, we realize the collective stimulation of the two dipolar corner states and their superposition state via far-field excitation. By stacking monolayer hexagonal boron nitride film onto the metasurface, we further achieve the topological phonon polaritons through the strong coupling between the corner state and the phonon, which is confirmed by the Rabi splitting as well as anticrossing behavior emerging in the transmission spectra.Furthermore, we reveal the robustness of the corner state and strong coupling by introducing defects into the metasurface. Finally, tunable corner state and strong coupling with on-demand control are realized by assembling monolayer graphene onto the metasurface. Our theoretical study proposes a unique hybrid-material platform for topological polaritonics and reconfigurable topological photonics, which can promote large-area topological applications in practice.