Zilong Tao

Fourier Neural Operators (FNOs) have gained increasing attention for their effectiveness in extracting frequencydomain features and efficiently approximating functions, making them wellsuited for classification tasks. However, the absence of specialized photonic hardware has limited the acceleration of FNO inference. In this study, we introduce what we believe is the first photonic hardware framework dedicated to speeding up the Fourier layer of an FNO. Our approach employs a frequency domain convolutional photonic chip and a micro-ring array chip, achieving 5-bit quantization precision in the inference process. On the Radio ML 2016.10b dataset, our Fourier convolutional neural network achieves a peak identification accuracy of 95.50%, outperforming standard convolution-based networks. These findings highlight the transformative potential of co-designing software and hardware, demonstrating how photonic computing can deliver specialized acceleration for critical AI components and substantially improve inference efficiency. Ultimately, this work lays a foundation for integrating photonic technologies into next-generation AI accelerators, pointing to a promising direction for further research and development in optoelectronic hybrid computing.

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. Moreover, a rigorous theoretical model is proposed to measure signal propagation in SiPhC and SiPhC-GRA, which incorporates all crucial linear and nonlinear optical effects, as well as influences of free-carriers and SL effects. BER results of multi-channel systems are evaluated by utilizing the Fourier series Karhunen-Loeve expansion method. Our findings reveal that good BER performance is acquired at SiPhCs and SiPhC-GRAs in SL regimes but with their footprint about 2.5-fold smaller than FL waveguides. Moreover, the enhanced nonlinearity in SiPhC-GRAs induced by strong graphene-SiPhC coupling causes extra signal degradation than SiPhCs at the same length. This work provides additional insights into the coupling effect between SiPhCs operating in SL regimes and graphene, and their influence on WDM signal transmission, highlighting the potential applications of SiPhC-GRA interconnects in next-generation super-computing systems.

Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. However, traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping and so on.

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. One common feature of these chiral metamaterials is that they all exhibit the weakest intensity but the strongest CD response in the third-order diffracted beams. Our work suggests that the DL model can predict CD performance of a 2D chiral nanostructure with a computational speed that is four orders of magnitude faster than RCWA but preserves high accuracy. The DL model introduced in this work shows great potentials in exploring various chiroptical interactions in metamaterials and accelerating the design of hypersensitive photonic devices.

Deep‐learning (DL) network has emerged as an important prototyping technology for the advancements of big data analytics, intelligent systems, biochemistry, physics, and nanoscience. Here, we used a DL model whose key algorithm relies on deep neural network to efficiently predict circular dichroism (CD) response in higher‐order diffracted beams of two‐dimensional chiral metamaterials with different parameters. To facilitate the training process of DL network in predicting chiroptical response, the traditional rigorous coupled wave analysis (RCWA) method is utilized. Notably, these T‐like shaped chiral metamaterials all exhibit the strongest CD response in the third‐order diffracted beams whose intensities are the smallest, when comparing up to four diffraction orders. Our comprehensive results reveal that by means of DL network, the complex and nonintuitive relations between T‐like metamaterials with different chiral parameters (i. e., unit period, width, bridge length, and separation length) and their CD performances are acquired, which owns an ultrafast computational speed that is four orders of magnitude faster than RCWA and a high accuracy. The insights gained from this study may be of assistance to the applications of DL network in investigating different optical chirality in low‐dimensional metamaterials and expediting the design and optimization processes for hyper‐sensitive ultrathin devices and systems.