# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 6 Conclusions  
Integrated photonic neural networks constitute an active research field with vast potential for growth and possibilities for diverse applications. In this work, we review the investigations that have recently emerged into photonic neural networks applied to optical signal equalization, in which the equalization of OOK, PAM2, PAM4, and OFDM signals has already been successfully experimentally demonstrated, confirming the effectiveness and robustness of photonic neural networks as an equalizer operating in the optical domain, with low power consumption, minimized latency, and bandwidth capacity in the Terahertz range. If manufactured in silicon, photonic neural networks can be compatible with CMOS technology, enabling high immunity to noise, a high level of integration, design simplicity, and operation over a wide range of voltage values. Future work aimed at equalizing optical communication signals involves overcoming existing implementation barriers of photonic neural networks, such as high insertion loss, that requires efficient amplification mechanisms; lack of flexibility of the device in processing different optical signals; and the need for training to still be in the electronic domain. There is also much to be investigated in evaluating the performance of photonic neural networks under more challenging optical equalization scenarios, which include equalizing optical signals with more complex modulation formats, higher transmission rates, and long transmission distances. Hybrid electronic-photonic neural network devices that combine the strengths of both domains are certainly another promising direction for future research. Finally, it can be expected that integrated photonic neural networks will become the next generation \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Photonic Neural Networks** & **Classical Neural Networks** \\ \hline \multirow{4}{*}{Complexity} & Can offer reduced complexity for certain operations & Well-established design methodologies and tools for low-complex architectures. However, increasing complexity leads to higher power consumption and latency [146]. \\  & nonlinearities can increase system complexity [66]. & Latency [146]. \\ \hline \multirow{4}{*}{Power consumption} & Potentially much lower for certain operations, especially linear computations. However, the power consumption of optical sources, modulators, and detectors needs to be considered [77]. & Can be high, especially for large and complex networks, due to electronic signal transmission and processing. Energy efficiency is a major concern [56]. \\ \hline \multirow{2}{*}{Speed} & Inherently high speed for signal propagation. Potential for ultra-fast processing for specific tasks [67]. & Limited by electronic signal propagation speed. Training and evaluating each node in large neural networks can be very time consuming [30]. \\ \hline \multirow{2}{*}{Scalability} & Challenges in scaling to large and complex networks & Well-established scaling techniques and manufacturing processes [35]. \\  & due to fabrication tolerances, signal losses, and low-flexibility architectures [80]. & \\ \hline \multirow{2}{*}{Nonlinearity} & Implementing efficient nonlinear activation functions & \multirow{2}{*}{Mature techniques for implementing nonlinear activation functions [28].} \\  & in the optical domain is a major challenge [103]. & \\ \hline \multirow{2}{*}{Training} & Training optical neural networks can be more challenging due to backpropagation limitations and integrated device constraints [76]. & \multirow{2}{*}{Mature training algorithms and software frameworks [25].} \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis between classical (operating in the electronic domain) and photonic neural networks (operating in the optical domain). of artificial neural networks, not only for equalizing optical communication signals but also for several other application scenarios in the coming years. L.C.B.S.