# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 5 Applications of Photonic Neural Networks for Equalizing Optical Communication Signals  
Photonic neural networks are in their early stages of development, so there are currently few scientific works on their use in equalizing optical communication signals. Therefore, in this section we present the investigations found in the scientific literature that address signal equalization using integrated photonic neural network devices more deeply. Initially, we can mention an investigation carried out in 2018 by [PERSON] et al. that demonstrated a simplified photonic reservoir computing approach for data classification of distorted optical signals after propagating over 45 km of optical fiber link [73]. In this work, a direct bit detection process was first converted into a pattern recognition problem. By employing the experimental photonic reservoir computing device, an improvement in the BER metric by two orders of magnitude for a pulse-amplitude modulation 2-level (PAM2) signal at 25 Gb/s was successfully achieved compared to directly classifying the transmitted signal, as depicted in Figure 8 [73]. Figure 8: BER metric as a function of the transmission distance in the short-reach communication system. Reproduced without modification from [PERSON] et al. [73], © 2018, licensed under a Creative Commons Attribution International License 4.0 (CC BY 4.0). In 2019, [PERSON] et al. presented the design and a numerical study of a photonic reservoir computing with 16 nodes operating in the optical domain [74]. Tests with signals transmitted at different data rates over optical fiber links of different lengths were evaluated by these authors. Particularly, for an on-off keying (OOK) signal at 40 Gb/s after 15 km of fiber propagation, a BER of 2 \(\times\) 10\({}^{-3}\) was obtained after applying the optical equalization with the proposed photonic reservoir computing [74]. Moreover, in 2021, [PERSON] et al. presented experimental results in which nonlinear distortions in a 32 Gb/s OOK signal were mitigated below the forward error correction (FEC) of 0.2 \(\times\) 10\({}^{-3}\) using a photonic reservoir computing [75]. Furthermore, the results obtained were compared with a delay line filter, clearly evidencing that the developed photonic device performed a nonlinear equalization, as shown in Figure 9 [75]. Using an optical perceptron, [PERSON] et al. [76] performed binary pattern recognition of signals at different rates, ranging from 5 to 16 Gb/s, achieving a BER as low as 10\({}^{-6}\). The perceptron performance after the training stage is illustrated in Figure 10. These experimental results demonstrate that the best performance was achieved for the 2-bit pattern. In the case of the 3-bit patterns, the best performance was achieved at 10 and 16 Gb/s, which are the closest bit rates to the 20 Gb/s rate of the designed photonic neural network. Thus, the versatility of this neural network is limited by its structure, which is designed to process optical signals at a specific data rate. More recently, in 2023, using photonic reservoir computing, [PERSON] et al. demonstrated a simultaneous improvement in the BER and the eye diagrams for an OOK signal with 25 Gb/s over a 25 km of single-mode fiber transmission. Employing the PSO training algorithm, it was possible to obtain fast convergence during the training of the network weights [79]. Due to the excellent performance of the training algorithm employed, a BER of 9 \(\times\) 10\({}^{-5}\) was accomplished, which is three orders of magnitude lower than that reached before the optical equalization [79]. Figure 9: Eye diagrams. (**a**) Distorted signal. (**b,d**) Signal after a delay line filter with (**b**) limited time-offset freedom and (**d**) full time-offset freedom. (**c,e**) Similar to (**b,d**) for the signals after equalization with photonic reservoir computing. Reproduced without modification from [PERSON] et al. [75], © 2021, licensed under the terms of the OSA Open Access Publishing Agreement. In the same year, [PERSON] et al.