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. For the first time, to the best of our knowledge, this algorithm encodes information in an integrated intensity modulation, effectively reducing the encoding difficulty. Moreover, the high reliability and scalability of optical encryption are ensured using both linear and nonlinear operations on photonic chips according to characteristics of MRRs. As the encryption and decryption experiments show, the image restoration accuracy of our optical encryption algorithm exceeds 99% under real system noise at the pixel level, indicating its noise-robust property. Meanwhile, the peak signal-to-noise ratios of the restored and encrypted images are >60 and <15 dB, respectively, revealing both the high accuracy of the restored image and the small correlation between the encrypted and original images. This work adds to the rapidly expanding field of optical image encryption on photonic chips.

Parallel edge extraction operators on chip speed up photonic convolutional neural networks.

We experimentally establish a 3 × 3 cross-shaped micro-ring resonator (MRR) array-based photonic multiplexing architecture relying on silicon photonics to achieve parallel edge extraction operations in images for photonic convolution neural networks. The main mathematical operations involved are convolution. Precisely, a faster convolutional calculation speed of up to four times is achieved by extracting four feature maps simultaneously with the same photonic hardware's structure and power consumption, where a maximum computility of 0.742 TOPS at an energy cost of 48.6 mW and a convolution accuracy of 95.1% is achieved in an MRR array chip. In particular, our experimental results reveal that this system using parallel edge extraction operators instead of universal operators can improve the imaging recognition accuracy for CIFAR-10 dataset by 6.2% within the same computing time, reaching a maximum of 78.7%. This work presents high scalability and efficiency of parallel edge extraction chips, furnishing a novel, to the best of our knowledge, approach to boost photonic computing speed.

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. Notably, the rigorous coupled wave analysis (RCWA) algorithm is first deployed to calculate circular dichroism (CD) in the higher-order diffraction beams and simultaneously promote the training of DNN. For the first time we creatively encode the material component and thickness of the metamaterials into the color images serving as input of the graphic-processable DNN, in addition to arbitrary graphical parameters. Especially, the smallest intensity is found in the third-order diffraction beams of E-like metamaterials, whose CD response turns out to be the largest. A comprehensive study is conducted to capture the influence of shape, unit period, thickness, and material component of arrays on chiroptical response. As expected, a satisfied precision and an accelerated computing speed that is 4 orders of magnitude quicker than RCWA are both achieved using DNN. This work belongs to one of the first attempts to thoroughly examine the generalization ability of the graphic-processable DNN for the study of arbitrary-shaped nanostructures and hypersensitive nanodevices.

Achieving efficient inverse design of low-dimensional heterostructures based on a vigorous scalable multi-task learning network.

A scalable multi-task learning (SMTL) model is proposed for the efficient inverse design of low-dimensional heterostructures and the prediction of their optical response. Specifically, several types of nanostructures, including single and periodic graphene-Si heterostructures consisting of n×n graphene squares (n=1∼9), 1D periodic graphene ribbons, 2D arrays of graphene squares, pure Si cubes and their periodic array counterparts, are investigated using both traditional finite element method and SMTL network, with the former providing training data (optical absorption) for the latter. There are two important algorithms implemented in SMTL model: one is the normalization mechanism that makes different parameters of different structures on the same scale, ensuring that SMTL network can deal with tasks with different dataset impartially and without bias; the other one is used to capture the impact of nanostructures' dimensions on their optical absorption and thus improve the generalization ability of SMTL. Utilizing SMTL model, we first study the absorption property of the multiple shaped nanostructures and look deeper into the impacts of n×n graphene squares and Si cuboid on the optical absorption of their heterostructures. Equally important, the multi-structure inverse design functionality of SMTL is confirmed in this context, which not only owns high accuracy, fast computational speed, and excellent generalizable ability, but also can be applied to contrive new structures with desired optical response. This work adds to the rapidly expanding field of inverse design in nanophotonics and establishes a multi-task learning framework for heterostructures and more complicated nanoparticles.

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. 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.

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. 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.