Photonic waveguide arrays provide a simple and versatile platform for simulating conventional topological systems. Here, we investigate a novel one-dimensional (1D) topological band structure, a dimer chain, consisting of silicon waveguides with alternating self-coupling and inter-coupling. Coupled mode theory is used to study topological features of such a model. It is found that topological invariants of our proposed model are described by the global Berry phase instead of the Berry phase of the upper or lower energy band, which is commonly used in the 1D topological models such as the Su–Schrieffer–Heeger model. Next, we design an array configuration composed of two dimer patterns with different global Berry phases to realize the topologically protected waveguiding. The topologically protected propagation feature is simulated based on the finite-difference time-domain method and then observed in the experiment. Our results provide an in-depth understanding of the dynamics of the topological defect state in a 1D silicon waveguide array, and may provide different routes for on-chip lightwave shaping and routing.

Inspired by recent rapid deep learning development, we present a convolutional-neural-network (CNN)-based algorithm to predict orbital angular momentum (OAM) mode purity in optical fibers using far-field patterns. It is found that this image-processing-based technique has an excellent ability in predicting the OAM mode purity, potentially eliminating the need of using bulk optic devices to project light into different polarization states in traditional methods. The excellent performance of our algorithm can be characterized by a prediction accuracy of 99.8% and correlation coefficient of 0.99994. Furthermore, the robustness of this technique against different sizes of testing sets and different phases between different fiber modes is also verified. Hence, such a technique has a great potential in simplifying the measuring process of OAM purity.