All-type optical logic gates using plasmonic coding metamaterials and multi-objective optimization.

In this article, we propose a multiport plasmonic system (MPS) for implementing all-type logic gates based on coding metamaterials and inverse design technology. Compared to traditional plasmonic logic gates, the coding metamaterials based on metal-dielectric-metal (MDM) structures provide powerful programmability for manipulating electromagnetic (EM) waves and have a compact footprint (0.8 µm × 1.1 µm) for integration. To improve the performance of logic gates, the nondominated sorting genetic algorithm version II (NSGA-II) are used to optimize the distributions of coding metamaterials. After the optimization, the simulation results show that all types of logic gates (AND, OR, NOT, NAND, NOR, XNOR, and XOR) can be obtained with an operating wavelength of 1.31 µm. The maximum extinction ratios between logic states "1" and "0" reach 10.15 dB, 57.54 dB, 43.25 dB, 20.76 dB, 10.42 dB, 24.04 dB, and 27.74 dB for the AND, OR, NOT, NAND, NOR, XNOR, and XOR gates, respectively. Moreover, wavelength-tunable logic operations are also demonstrated to work within a wide spectrum. Our proposed plasmonic system not only provides a universal scheme for implementing all-type compact logic gates for optical processing and computing but also demonstrates effective applications of inverse design in nanophotonic devices.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency.

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonics devices. We briefly review recent progress of this field of research and show data-driven applications, including spectrum prediction, inverse design and performance optimization, for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency (PIT) effect, which can be theoretically demonstrated by using the transfer matrix method. Some traditional machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, evolutionary algorithms, including single-objective (genetic algorithm) and multi-objective optimization (NSGA-II), are used to achieve the steep transmission characteristics of PIT effect by synthetically taking many different performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum reaches 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonics devices based on machine learning and evolutionary algorithms and a reference for the selection of machine learning algorithms for simple inverse design problems.

Efficient training and design of photonic neural network through neuroevolution.

Recently, optical neural networks (ONNs) integrated into photonic chips have received extensive attention because they are expected to implement the same pattern recognition tasks in electronic platforms with high efficiency and low power consumption. However, there are no efficient learning algorithms for the training of ONNs on an on-chip integration system. In this article, we propose a novel learning strategy based on neuroevolution to design and train ONNs. Two typical neuroevolution algorithms are used to determine the hyper-parameters of ONNs and to optimize the weights (phase shifters) in the connections. To demonstrate the effectiveness of the training algorithms, the trained ONNs are applied in classification tasks for an iris plants dataset, a wine recognition dataset and modulation formats recognition. The calculated results demonstrate that the accuracy and stability of the training algorithms based on neuroevolution are competitive with other traditional learning algorithms. In comparison to previous works, we introduce an efficient training method for ONNs and demonstrate their broad application prospects in pattern recognition, reinforcement learning and so on.