Advancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams for high-capacity data transmission. Conventional electronic convolutional neural networks exhibit constraints in efficiently demultiplexing OAM signals. Here, we introduce a hybrid optical-electronic convolutional neural network that is capable of completing Fourier optics convolution and realizing intensity-recognition-based demultiplexing of multiplexed OAM beams under variable simulated atmospheric turbulent conditions. The core part of our demultiplexing system includes a 4F optics system employing a Fourier optics convolution layer. This optical spatial-filtering-based convolutional neural network is utilized to realize the training and demultiplexing of the 4-bit OAM-coded signals under simulated atmospheric turbulent conditions. The current system shows a demultiplexing accuracy of 72.84% under strong turbulence scenarios with 3.2 times faster training time than all electronic convolutional neural networks.

Graphene, renowned for its exceptional electrical, optical, and mechanical properties, takes center stage in the realm of next-generation electronics. In this paper, we provide a thorough investigation into the comprehensive fabrication process of graphene field-effect transistors. Recognizing the pivotal role graphene quality plays in determining device performance, we explore many techniques and metrological methods to assess and ensure the superior quality of graphene layers. In addition, we delve into the intricate nuances of doping graphene and examine its effects on electronic properties. We uncover the transformative impact these dopants have on the charge carrier concentration, bandgap, and overall device performance. By amalgamating these critical facets of graphene field-effect transistors fabrication and analysis, this study offers a holistic understanding for researchers and engineers aiming to optimize the performance of graphene-based electronic devices.

The exponential growth in data traffic has driven significant research into maximizing the capacity of free-space optical (FSO) communication systems. Orbital angular momentum (OAM) multiplexing offers a promising approach by using spatially structured beams with helical wavefronts to achieve higher data transmission rates. However, conventional electronic convolutional-neural-network-based OAM demultiplexing schemes exhibit substantial computational and energy efficiency limitations. In this paper, we introduce a hybrid optical-electronic Fourier phase shift neural network that implements phase-only feature extraction of the input multiplexed OAM beams in the Fourier domain. The proposed hybrid neural network uses phase spatial frequency kernels with the spatial light modulator to perform additive phase modulation of the Fourier-transformed input beams. Experimental results show that the proposed phase shift neural network has 6.5 times faster training time and three orders of magnitude higher energy efficiency compared to the designed conventional all-electronic convolutional neural network with one single convolution layer. The proposed system represents an idea towards energy-efficient, high-throughput optical neural networks for OAM-based FSO communication systems.