# A Review of Optical Neural Networks
## 1 Introduction  
Deep learning has been integrated into various subject fields in recent years, allowing the rejuvenation of fields that had previously reached their development limits. Deep learning has gained momentum in applications including image processing [1], physics [2], and natural language processing [3]. In addition, it is being used as a foundation to develop custom industrial applications rapidly. Deep learning is the process of learning the intrinsic laws and representation levels of sample data and combining low-level features to form more abstract high-level representations of attribute categories or features by building a neural network. It can mimic the behavior of the human brain in the process of analytical learning to discover distributed feature representations of data, allowing complex learning tasks such as classification to be performed with simple models [4; 5]. The tools that support deep learning have existed for decades and have evolved continuously over the past few years due to the availability of large datasets and hardware developments. High-quality algorithms, such as neural networks for deep learning, have become an essential driver regarding the technological aspects of artificial intelligence [6; 7; 8]. Three major factors are essential for developing deep learning approaches and technologies: data, algorithms, and computational capacity. Data processing and algorithm implementation require the technical support of the chips in the application terminal, with current deep learning chip technologies primarily including graphics processing units, field-programmable gate arrays, and application-specific integrated circuits. With the dramatic increase in the amount of data to be processed, the bottlenecks of traditional electrical chip technology such as the von Neumann architecture or the available complementary metal oxide semiconductor (CMOS) process and devices are emerging, limiting the application and popularity of deep learning by introducing the problems of chip power consumption and performance enhancement. These limitations make it challenging to adopt deep learning in end equipment [9]. Neuromorphic systems are the systems that attempt to map machine learning and artificial intelligence algorithms into large-scale distributed hardware capable of simulating the human brain at the physical level for computation and simulation purposes. In the past, neuromorphic system applications have been comprehensively studied within the field of electronics, which has ultimately resulted in the rendering andrefinement of innovations such as the memristor, the field-effect transistor, as well as a host of other neuromorphic-based chip devices. Recently, owing to the prominence of the von Neumann bottleneck, the inherent difficulties of electronic wiring, and the rapid growth of chip power consumption, people have started to focus more upon optical neuromorphic systems as alternatives. Optical neural networks and photonic circuits can provide a novel dedicated neural network accelerator scheme to exploit the parametric changes of optics for computing. Optical computing systems can be massively parallel or combined with small form factor devices. Photonics offers the advantages of high speed, high bandwidth, and low power consumption compared with electronics. Photonic solutions can significantly reduce the power consumption of logic and data operations [10, 11, 12]. It is well known that an ordinary lens can perform a Fourier transform without consuming any energy and that certain matrix operations can be performed with no energy consumption. Many inherent optical nonlinearities can be directly used to implement nonlinear operations with photons. Once a neural network has been trained, the architecture can be passive, and computations on the optical signal can be performed without additional energy input [13].