# A Review of Optical Neural Networks
## 3 Development Processes and Categories  
### Silicon-Based Optical Neural Networks  
More recently, the rapid development of silicon optical integration technology has provided technical support for research into photonic integrated neural network chips. Photonic integrated neural networks are used to perform matrix multiplication and addition calculations (MACs) in neural networks, using photon integration technology to run deep learning algorithms and implement machine learning-related applications. Conceptual exploratory research work has already been conducted on the use of photonic integrated neural network chips to implement neural network operations, and there is the hope of using photonic integrated neural network chips to accomplish tasks such as image classification and object detection for applications in servers, autonomous driving, and other scenarios. Representative research schemes include the Mach-Zehnder modulator scheme [33; 34; 35; 36; 37; 38], micro-loop modulator scheme [39; 40], and the three-dimensional integration scheme [41]. Such research has focused on simulation modeling and experimental applications of photonic neural network chips. In 2019, Lightelligence released the world's first prototype board for photonic integrated neural network chips, which were then used to implement the MNIST handwritten digital dataset recognition application with a recognition accuracy of over 90% and a speed approximately two orders of magnitude faster than that of traditional electric chips, encouraging photonic integrated neural network chip applications and development. The number of Mach-Zehnder interferometers (MZIs) is proportional to the square number of elements N in a vector and is a necessary side effect of solving arbitrary matrices [33; 36]. Loss, noise, and defects are serious problems that affect a system as the size of a photonic circuit increases [42]. Contemporary deep neural network architectures consist of linear cascades of layers followed by multiple iterations of nonlinear activation functions. Nonetheless, meshes based on devices such as MZIs, while capable of arbitrary matrix multiplication, cannot perform important nonlinear operations. As an alternative to MZI-based MACs, SiN-conjugated waveguides can be used to construct a fully optical neurosynaptic network that is based on phase change materials (PCMs) [43; 44; 45; 46; 47]. By varying the number of optical pulses to set the weights randomly, the time delay can modulate the weights to achieve a nonlinear activation function similar to a rectified linear unit with a synaptic plasticity consistent with the well-known Hebbian learning or spike-dependent plasticity rule [48]. Optical neural networks are based on silicon integration as shown in Figure 1.  
Figure 1: Implementing a neural network based on silicon integration. (**a**) A silicon photonic integrated circuit comprising programmable MZIs realizes the optical interference unit. This architecture enables the operation of the weight matrix using the MZIs through singular value decomposition [33]. (**b**) Using the micro-ring resonator as the neuron of the broadcast and weight network, the wavelength division multiplexed signals are weighted using reconfigurable continuous value filters and then summed for total power detection [40]. (**c**) A biplane signal distribution network is realized through the stacking of waveguides, which can complete the all-around connection of the feedforward neural network [41]. (**d**) Photonic synapses are fabricated using PCM in combination with an integrated silicon nitride waveguide, and the synaptic weights are set randomly by varying the number of optical pulses sent by the waveguide [43]. Photonic neural networks offer a promising alternative to microelectronic and hybrid optoelectronic implementations, where classical neural networks rely heavily on fixed matrix multiplication.