High-Q two-groove resonator for all-dielectric Bloch surface wave platform

We propose a simple integrated resonant structure for the Bloch surface wave platform, which consists of two subwavelength grooves patterned on the surface of a one-dimensional dielectric photonic crystal. We demonstrate that the investigated structure can operate in a parasitic-scattering-free regime and, in this case, provide unity transmittance and zero reflectance at resonance conditions associated with the excitation of a leaky mode of the structure localized at the central ridge formed by the grooves. The proposed structure may find application in integrated photonic devices for optical filtering and analog optical computing.

Tunable GaAs metasurfaces for ultrafast image processing

The design and construction of optical semiconductor metasurfaces for various applications have become an important topic in the last decade. However, most metasurfaces are static; they are optimized for only one exact purpose and typically realize only one operation. In this work, we discuss the basic methods for creating dynamic metasurfaces giving special attention to ultrafast optical switching and provide numerical modeling of metasurfaces made of GaAs material realizing different amplitude-phase profiles under asymmetrical optical pumping. The metasurfaces are composed of semiconductor discs immersed in a fused silica medium. We demonstrate that based on Fourier transform and spatial filtering methods, these structures can be used for image processing and optical computing. Ultrafast switching is achieved by using an optical pump-probe scheme. The characteristic relaxation times between the pumped state and the relaxed state are on the order of several picoseconds.

Optical Multi-Imaging-Casting Accelerator for Fully Parallel Universal Convolution Computing

Over the last few years, optical computing has become a potential solution to computationally heavy convolution, aimed at accelerating various artificial intelligence applications. However, past schemes have never efficiently realized fully parallel optical convolution. Here, we propose a new paradigm for a universal convolution accelerator with truly massive parallelism and high precision based on optical multi-imaging-casting architecture. Specifically, a two-dimensional Dammann grating is adopted for the generation of multiple displaced images of the kernel, which is the core process for kernel sliding on the convolved matrix. Our experimental results indicate that the computing accuracy is typically close to 8-bit, and this accuracy can be improved further by using hybrid analog–digital coding method. In addition, a convolutional neural network for the standard MNIST dataset is demonstrated, and the recognition accuracy for inference is up to 97.3%. The paradigm reported here will open new opportunities for high-throughput universal convolution accelerators for real-time or quasi-real-time AI applications.

The challenges of modern computing and new opportunities for optics

In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.