# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Fiber-Based Optical Neural Networks
#### 3.3.3 Time Stretch
Such an approach enables optically controllable computation within nonlinear optical fibers. Meanwhile, optical neural networks of optical fibers have good energy efficiency and scalability, providing a favorable approach for photonic neuromorphic computations. Photonic integration renders ultrafast artificial neural networks possible, and photonic neuromorphic computing results in challenges different from those of electronic computing. Algorithms running on such hardware can meet the growing demand for deep learning and artificial intelligence in areas such as medical diagnostics, telecommunications, and high-performance and scientific computing. All calculations will be performed at the speed of light, which is much faster than the analog speed of digital computers, therefore processing exponentially more data. For real-time observations, time-stretch processors are ideal output devices. Even though photonic neuromorphic computing is still in its infancy, this research area has great potential to expand the frontiers of deep learning and information processing. Figure 5: (**A**) PTS schematic diagram of principle. (**B**) OTS-QPI system operational process [104]. In this article, we highlight the diversity of optical neural networks. Optical neural networks are a rapidly growing field, with many schemes currently under investigation, all of which have their own advantages and disadvantages, as shown in Table 1.  
Photonic neuromorphic computing is a system comprising active and passive devices, light sources, and transistors. However, no fabrication platform is yet capable of implementing all of these functions upon a single mold based on current fabrication levels. Present optical neural network solutions still exclusively rely on electronics. Optoelectronic integration processes remain challenging because submicron-scale etching techniques are still unavailable, and issues such as height disparities, spacing restrictions, and process incompatibilities are still prevalent. Currently, on-chip light sources use a concatenation of III-V families of materials or a direct epitaxy of quantum dot lasers on silicon. However, the complexity of the preparation process and associated reliability cannot meet the commercial mass production standards. Several approaches for integrating photonic systems and CMOS sensors for ultimately achieving a successful on-chip photonic neural network design are presently being explored. Difficulties associated with on-chip optical delays are also a challenge being evaluated currently. Nonlinear operations are an indispensable and important part of neuromorphic computing. Moreover, nonvolatile photonic storage and weighting as well as low power consumption are necessary to ensure neurosynaptic function. Although photonic nonlinear neural operations have been demonstrated in materials such as PCM, a large gap still remains regarding electronic nonlinear neuromorphic operations. Accordingly, there is still room for development in energy efficiency and fast switching of new accumulable materials. In addition, training is one of the key steps for the vast majority of neural network algorithms, which to date still requires implementation. Optical neural networks have been shown to be a promising energy-efficient method for implementing matrix multiplication [33; 34]. However, these neural networks must be trained to be applied in practice, and they cannot be applied in hardware where information can flow in only the forward direction. Finally, current photonic platforms lack the common and important storage in electronic computers. In adaptive learning and training, weights need to be updated frequently, a situation that requires fast memories such as Dynamic Random Access Memory (DRAM), Resistive Random Access Memory (RRAM), and Ferroelectric Random Access Memory (FRAM). In summary, neuromorphic photonic systems can follow the same architecture, and the fabrication of stand-alone all-optical computers is therefore unlikely, owing to the lack of photonic memory devices.