Polarization-insensitive 1D unidirectional compact grating coupler for the C-band using a 500 nm SOI

Polarization independent silicon micro antenna based on a subwavelength metamaterial

Optical antennas are key components of an optical phased array system, enabling light coupling between the chip and the free space. In such systems, surface gratings are commonly used as antenna elements, which however suffer from a strong polarization sensitivity of their scattering angle and efficiency. Here, we propose a versatile approach to realize micro antennas based on surface gratings with a polarization insensitive behavior exploiting a subwavelength metamaterial in the silicon-on-insulator platform. In the experimental demonstration, the antenna successfully achieves the same diffraction angle of 10° for both TE and TM polarizations and an estimated scattering efficiency of -4 dB despite a very compact footprint of 6.4 [Formula: see text] x 2.9 [Formula: see text]. The difference in diffraction efficiency between the two polarizations remains smaller than 1 dB over a bandwidth of 31 nm.

Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks

The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural networks provide a possible solution to this challenge. They can outperform conventional silicon-based electronic neural networks due to the significantly higher speed of the propagation of optical signals (≈108 m.s-1) compared to electrical signals (≈105 m.s-1), their parallelism in nature, and their low power consumption. The integrated diffractive deep neural network (ID2NN) uses an on-chip fully passive photonic approach to achieve the functionality of neural networks (matrix-vector operations) and can be fabricated via the CMOS process, which is technologically more amenable to implementing an artificial intelligence processor. In this paper, we present a detailed design framework for the integrated diffractive deep neural network and corresponding silicon-on-insulator integration implementation through Python-based simulations. The performance of our proposed ID2NN was evaluated by solving image classification problems using the MNIST dataset.