Co-design for Kolmogorov-Arnold networks to unlock the full potential of optical intelligent accelerators.

The explosive growth in computational demands of artificial neural networks has spurred research into optical neural networks. However, most existing work overlooks the co-design of software and hardware, resulting in challenges with data encoding and nonlinear activation in optical neural networks, failing to fully leverage the potential of optical computing hardware. In this work, we propose a nonlinear optical processing unit (NL-OPU) based on the nonlinear response of Mach-Zehnder modulators (MZMs) for an optical Kolmogorov-Arnold network (OKAN), which bypasses the challenges related to linear data representation and nonlinear activation execution in optical neural networks.

Silicon photonic convolution operator exploiting on-chip nonlinear activation function.

Nonlinear activation functions (NAFs) are essential in artificial neural networks, enhancing learning capabilities by capturing complex input-output relationships. However, most NAF implementations rely on additional optoelectronic devices or digital computers, reducing the benefits of optical computing. To address this, we propose what we believe to be the first implementation of a nonlinear modulation process using an electro-optic IQ modulator on a silicon photonic convolution operator chip as a novel NAF.

Computing dimension for a reconfigurable photonic tensor processing core based on silicon photonics.

In the rapidly evolving field of artificial intelligence, integrated photonic computing has emerged as a promising solution to address the growing demand for high-performance computing with ultrafast speed and reduced power consumption. This study presents what we believe is a novel photonic tensor processing core (PTPC) on a chip utilizing wavelength division multiplexing technology to perform parallel multiple vector-matrix multiplications concurrently, allowing for reconfigurable computing dimensions without changing the hardware scale. Specifically, this architecture significantly enhances the number of operations in convolutional neural networks, making it superior to other photonic computing systems.

On-chip silicon photonic micro-ring processor lights up optical image encryption.

Optical image encryption has long been an important concept in the fields of photonic network processing and communication. Here, we propose a convolution-like operation-based optical image encryption algorithm exploiting a silicon photonic multiplexing architecture to achieve content security. Particularly, the encryption process is completed in a 3 × 3 cross-shaped photonic micro-ring resonator (MRR) array on chip.

Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials.

A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations.

Graphic-processable deep neural network for the efficient prediction of 2D diffractive chiral metamaterials.

We propose a novel, to the best of our knowledge, graphic-processable deep neural network (DNN) to automatically predict and elucidate the optical chirality of two-dimensional (2D) diffractive chiral metamaterials. Four classes of 2D chiral metamaterials are studied here, with material components changing among Au, Ag, Al, and Cu. The graphic-processable DNN algorithm can not only handle arbitrary 2D images representing any metamaterials that may even go beyond human intuition, but also capture the influence of other parameters such as thickness and material composition, which are rarely explored in the field of metamaterials, laying the groundwork for future research into more complicated nanostructures and nonlinear optical devices.

BER evaluation in a multi-channel graphene-silicon photonic crystal hybrid interconnect: a study of fast- and slow-light effects.

A comprehensive theoretical investigation on the bit-error ratio (BER) performance of multi-channel photonic interconnects operating in pulsed regimes is presented. Specifically, the optical link contains either a silicon photonic crystal (SiPhC) or a SiPhC-graphene (SiPhC-GRA) waveguide, possessing slow-light (SL) and fast-light (FL) regimes. A series of Gaussian pulses plus complex white noise are placed at input of each channel, with output signals demultiplexed and analyzed by a direct-detection receiver.

Optical circular dichroism engineering in chiral metamaterials utilizing a deep learning network.

Here, a deep learning (DL) algorithm based on deep neural networks is proposed and employed to predict the chiroptical response of two-dimensional (2D) chiral metamaterials. Specifically, these 2D metamaterials contain nine types of left-handed nanostructure arrays, including U-like, T-like, and I-like shapes. Both the traditional rigorous coupled wave analysis (RCWA) method and DL approach are utilized to study the circular dichroism (CD) in higher-order diffraction beams.