# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 4 Building Blocks of Photonic Neural Networks
Particularly, implementing activation functions remains challenging due to the linear nature of optical components. However, it can be overcome by employing electro-optical modulators that modulate the intensity of the optical signal based on electrical inputs. Multiplication operations, especially matrix multiplications, can be performed using optical components such as beam splitters and interferometers, where weights and inputs are optically encoded, allowing the network to be dynamically reprogrammed and trained. Training photonic neural networks, in turn, requires parameter adjustments based on error gradients, which still requires the combination of electrical circuits to perform back-propagation and update the weights based on the computed errors [113]. In this scenario, integrating these components in all-optical signal processing follows steps that range from converting input data into optical signals by modulators that translate electrical signals into optical ones. Next, they undergo linear transformations, which can be performed by matrix multiplications. Then, they are subjected to nonlinear activation functions implemented by electro-optical modulators, introducing the necessary nonlinearity. Finally, error gradients must be calculated to adjust the optical components of the photonic neural network during training, ensuring learning of the desired task [113]. The extraordinary advances in photonics have brought to light a promising new path for the next generation of artificial intelligence technologies, paved by integrated photonic devices. Among the most promising avenues encompassing technologies and methods for advancing photonic neural networks in optical communications, we can highlight the use of microring resonators [114], cascaded-array programmable Mach-Zehnder interferometers [115], spatial light modulators and Fourier lenses [116], phase-change materials [117], microcomb sources [118], and vertical-cavity surface-emitting laser arrays [119]. Particularly, phase-change materials can change their optical properties in response to external stimuli, enabling the development of reconfigurable photonic devices, where the synapses are built of optical waveguides, and weighting is achieved via phase-change material cells [71]. This adaptability is crucial for creating dynamic and efficient photonic neural networks capable of processing communication signals of different formats. The most common phase-change material is the archetypal alloy Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\)[120], but other compositions are also being investigated [121]. It is also worth highlighting that vertical-cavity surface-emitting laser arrays can operate with low energy consumption (<5 aJ per symbol) and rapid programmability, with high-speed updating of weights, constituting a promising candidate for photonic neural network applications [119]. Optical frequency comb devices have attracted significant attention in photonics [122]. Comb technologies from the near-ultraviolet to the mid-infrared spectral range have been investigated in photonic-chip waveguides via supercontinuum generation and in microresonators via Kerr comb generation [122]. Thin-film lithium niobate-on-insulator has arisen as a photonic substrate that enables photonic circuits with dimensions compatible with silicon photonic devices [123] and provides the ability to integrate fast electro-optical modulators and efficient nonlinear optical elements on the same chip. Specifically, lithium niobate-based modulators can deliver very high modulation frequencies [124] and support data rates up to 210 gigabits per second with low on-chip optical loss [125]. In this scenario, investigations have also emerged on integrated photonic digital-to-analog converters capable of operating without optic-electric-optic domain crossing [126]. Recently, [PERSON] et al.