It is conventionally assumed that the growth of monodisperse colloidal nanocrystals requires a temporally discrete nucleation followed by monomer attachment onto the existing nuclei. However, recent studies have reported violations of this classical growth model, and have suggested that inter-particle interactions are also involved during the growth. Mechanisms of nanocrystal growth still remain controversial. Using in situ transmission electron microscopy, we show that platinum nanocrystals can grow either by monomer attachment from solution onto the existing particles or by coalescence between the particles. Surprisingly, an initially broad size distribution of the nanocrystals can spontaneously narrow. We suggest that nanocrystals take different pathways of growth based on their size- and morphology-dependent internal energies. These observations are expected to be highly relevant for other nanocrystal systems.

Observation of Single Colloidal Platinum Nanocrystal Growth Trajectories

The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of nonlinear relationships in high-dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data-science concepts framed within the context of photonics are also discussed, including the network-training process, delineation of different network classes and architectures, and dimensionality reduction. Neural networks can capture nonlinear relationships in high-dimensional spaces and are powerful tools for photonic-system modelling. This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers.

Deep neural networks for the evaluation and design of photonic devices

The data sciences revolution is poised to transform the way photonic systems are simulated and designed. Photonics are in many ways an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of non-linear relationships in high dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we will show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We will also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data sciences concepts framed within the context of photonics will also be discussed, including the network training process, delineation of different network classes and architectures, and dimensionality reduction.

Optical sensors are widely used for refractive index measurement in biomedical, chemical, and food processing industries and offer high sensitivity to ambient refractive index variations due to the specific field distribution of the resonances. The sensor's sensitivity is greatly dependent on its material and structure. In this review, we focused on plasmonic refractive index sensors with no fluorescent labelling required. The latest developments on special types of plasmonic structures such as metal-insulator-metal waveguides and their application in refractive index sensors are presented. Moreover, numerous types of plasmonic waveguides, geometries, materials, fabrication processes, and losses related to plasmonic waveguides are explained to provide a better understanding of this topic. The highlight of this review is to discuss the spectral performance of recently reported refractive index sensors by emphasising their sensitivity and figure of merits.

Plasmonic sensors based on Metal-insulator-metal waveguides for refractive index sensing applications: A brief review

The growing maturity of nanofabrication has ushered massive sophisticated optical structures available on a photonic chip. The integration of subwavelength-structured metasurfaces and metamaterials on the canonical building block of optical waveguides is gradually reshaping the landscape of photonic integrated circuits, giving rise to numerous meta-waveguides with unprecedented strength in controlling guided electromagnetic waves. Here, we review recent advances in meta-structured waveguides that synergize various functional subwavelength photonic architectures with diverse waveguide platforms, such as dielectric or plasmonic waveguides and optical fibers. Foundational results and representative applications are comprehensively summarized. Brief physical models with explicit design tutorials, either physical intuition-based design methods or computer algorithms-based inverse designs, are cataloged as well. We highlight how meta-optics can infuse new degrees of freedom to waveguide-based devices and systems, by enhancing light-matter interaction strength to drastically boost device performance, or offering a versatile designer media for manipulating light in nanoscale to enable novel functionalities. We further discuss current challenges and outline emerging opportunities of this vibrant field for various applications in photonic integrated circuits, biomedical sensing, artificial intelligence and beyond.

Optical meta-waveguides for integrated photonics and beyond.

A plasmonic refractive index (RI) sensor based on metal-insulator-metal (MIM) waveguide coupled with concentric double rings resonator (CDRR) is proposed and investigated numerically. Utilizing the novel supermodes of the CDRR, the FWHM of the resonant wavelength can be modulated, and a sensitivity of 1060 nm/RIU with high figure of merit (FOM) 203.8 is realized in the near-infrared region. The unordinary modes, as well as the influence of structure parameters on the sensing performance, are also discussed. Such plasmonic sensor with simple framework and high optical resolution could be applied to on-chip sensing systems and integrated optical circuits. Besides, the special cases of bio-sensing and triple rings are also discussed.

https://www.mdpi.com/1424-8220/18/1/116/pdf

Plasmonic Refractive Index Sensor with High Figure of Merit Based on Concentric-Rings Resonator.

Based on the traditional directional coupler, we proposed a scheme to design on-chip polarization beam splitters using an inverse design method. In our scheme, the coupling area of the designed devices are only 0.48  μm×6.4  μm. By manipulating the refractive index of the coupling region, the devices can work in C-band, L-band, O-band, or any other communication band. Different from conventional design methods, which need to adjust the design parameters artificially, if the initial conditions are determined, the proposed scheme can automatically adjust the design parameters of devices according to specific requirements. The simulation results show that the insertion losses of the designed polarization beam splitters can be less than 0.4 dB (0.35 dB) for TE (TM) mode at the wavelengths of 1310, 1550, and 1600 nm, and the extinction ratios are larger than 19.9 dB for the TE and TM modes at all three wavelengths. Besides, the extinction ratios of both polarization states are more than 14.5 dB within the wavelength range of 1286–1364 nm, 1497–1568 nm, and 1553–1634 nm. At the same time, the insertion losses are smaller than 0.46 dB.

Ultra-compact broadband polarization beam splitter with strong expansibility

All-optical multi-channel switching at telecommunication wavelengths based on tunable plasmon-induced transparency

Implementation of on-chip multi-channel focusing wavelength demultiplexer with regularized digital metamaterials

A ring resonator is a basic component of traditional photonic integrated circuits (PIC), which has been, however, found difficult to be applied efficiently in high-compact plasmonic metal-insulator-metal (MIM) systems. Here, based on a plasmonic band-stop filter with a square ring resonator (SRR), a novel side-coupling method is introduced both numerically and theoretically to achieve a drop in the resonant wavelength in the SRR with considerable efficiency. By introducing the reflector structure, the performance can be appreciably improved. Besides, this structure also has potential for sensing and switching. Finally, a dual demultiplexer based on SRRs is realized at telecommunication wavelengths with comparable performance, which makes it possible to apply ring resonators in on-chip plasmonic wavelength division multiplex (WDM) networks. This work is valuable for PIC design, and will promote the on-chip plasmonic system progress.

Yunxin Han

Papers

Plasmonic Refractive Index Sensor with High Figure of Merit Based on Concentric-Rings Resonator.

Ultra-compact broadband polarization beam splitter with strong expansibility

All-optical multi-channel switching at telecommunication wavelengths based on tunable plasmon-induced transparency

Implementation of on-chip multi-channel focusing wavelength demultiplexer with regularized digital metamaterials

Plasmonic Filter and Demultiplexer Based on Square Ring Resonator