Mode-division multiplexing technology using the high-order modes of multimode waveguides enables high-bandwidth data transmission. High-speed mode channel switching is a pivotal function for these optical networks. Here, we propose a modal switching scheme on a silicon-on-insulator platform and demonstrate a high-speed two-mode switch that exploits a Y-junction and multimode interference structure. The design allows for simultaneous switching of two optical modes. A PN-doped junction-based phase shifter in one branch of a Y-junction enables dynamic switching in 2.5 ns. The measured switching extinction ratio is 12.5 dB or better with an open eye diagram for a 10 Gb/s on–off key optical payload signal. The optical power penalty is within 0.5 dB for the two-mode switching at a bit error rate of ${10}^{-9}$. This two-mode switch could enable on-chip mode-based switching network topology for greater aggregated throughput capacity.

This paper presents the performance analysis of a phase error- and loss-tolerant multiport field-programmable MZI-based structure for optical neural networks (ONNs). Compared to the triangular (Reck) mesh, our proposed diamond mesh makes use of a larger number of MZIs, leading to a symmetric topology and adding additional degrees of freedom for the weight matrix optimization in the backpropagation process. Furthermore, the additional MZIs enable the diamond mesh to optimally eliminate the excess light intensity that degrades the performance of the ONNs through the tapered out waveguides. Our results show that the diamond topology is more robust to the inevitable imperfections in practice, i.e., insertion loss of the constituent MZIs and the phase errors. This robustness allows for better classification accuracy in the presence of experimental imperfections. The practical performance and the scalability of the two structures implementing different sizes of optical neural networks are analytically compared. The obtained results confirm that the diamond mesh is more error- and loss-tolerant in classifying the data samples in different sizes of ONNs.

A complete review of the data vortex optical packet switched (OPS) interconnection network architecture is presented. The distributed multistage network topology is based on a banyan structure and incorporates a deflection routing scheme ideally suited for implementation with optical components. An implemented 12-port system prototype employs broadband semiconductor optical amplifier switching nodes and is capable of successfully routing multichannel wavelength-division multiplexing packets while maintaining practically error-free signal integrity (BER < 10^-12) with median latencies of 110 ns. Packet contentions are resolved without the use of optical buffers via a distributed deflection routing control scheme. The entire payload path in the optical domain exhibits a capacity of nearly 1 Tb/s. Further experimental measurements investigate the OPS interconnection network's flexibility and robustness in terms of optical power dynamic range and network timing. Subsequent experimental investigations support the physical layer scalability of the implemented architecture and serve to substantiate the merits of the data vortex OPS network architectural paradigm. Finally, modified design considerations that aim to increase the network throughput and device-level performance are presented.

Implementing any linear transformation matrix through the optical channels of an on-chip reconfigurable multiport interferometer has been emerging as a promising technique for various fields of study, such as information processing and optical communication systems. Recently, the use of multiport optical interferometric-based linear structures in neural networks has attracted a great deal of attention. Optical neural networks have proven to be promising in terms of computational speed and power efficiency, allowing for the increasingly large neural networks that are being created today. This paper demonstrates the experimental analysis of programming a 4×4 reconfigurable optical processor using a unitary transformation matrix implemented by a single layer neural network. To this end, the Mach-Zehnder interferometers (MZIs) in the structure are first experimentally calibrated to circumvent the random phase errors originating from fabrication process variations. The linear transformation matrix of the given application can be implemented by the successive multiplications of the unitary transformation matrices of the constituent MZIs in the optical structure. The required phase shifts to construct the linear transformation matrix by means of the optical processor are determined theoretically. Using this method, a single layer neural network is trained to classify a synthetic linearly separable multivariate Gaussian dataset on a conventional computer using a stochastic optimization algorithm. Additionally, the effect of the phase errors and experimental equipment inaccuracies and the device components imperfections is also analyzed and simulated. Finally, the optical processor is experimentally programmed by applying the obtained phase shifts from the matrix decomposition process to the corresponding phase shifters in the device. The experimental results show that the optical processor achieves 72% classification accuracy compared to the 98.9% of the simulated optical neural network on a digital computer.

Reconfigurable photonic integrated devices and circuits are very important for many applications. Among various mechanisms for realizing reconfigurability, thermo-optic effect is popular because of the availability for many materials, the design simplicity and the fabrication ease. In particular, silicon has a large thermo-optic coefficient as well as a high heat conductivity, and thus it is promising to realize efficient thermally-reconfigurable silicon photonic integrated devices and circuits. Recent progress is reviewed in this paper. First, thermally-tunable silicon photonic filters based on different structures are summarized, including microring resonators (MRRs) and MRRs-assisted Mach-Zehnder interferometers (MZIs). Second, silicon photonic switches based on MZIs and MRRs are reviewed. Finally, a review is given for thermally-reconfigurable silicon photonic integrated circuits developed for the applications in quantum photonics, microwave photonics, and optical interconnects.