## 7 Role of Artificial Intelligence in the Development of Integrated Photonics  
Artificial intelligence (AI) was developed to emulate human cognitive functions such as learning, reasoning, and problem-solving, aiming to enhance efficiency, automate repetitive tasks, and solve complex problems across various industries [167; 168]. The inception of AI dates back to the mid-20 th century, with significant milestones including [PERSON]'s conceptual framework and the development of the first AI programs in the 1950s and 1960s [169; 170]. AI's application in photonics--a field concerned with the generation, manipulation, and detection of light--began gaining traction in the early 21 st century. By leveraging AI, researchers have been able to optimize photonic devices and systems, enhance imaging techniques, and improve the design and fabrication processes. This integration has led to advancements in telecommunications, medical imaging, and quantum computing, among other areas. AI and machine learning (ML) algorithms stand poised to revolutionize integrated photonics by enhancing device design, optimization, and control processes [171]. With its ability to analyze vast amounts of data and complex systems, AI can optimize the performance of photonic devices, leading to unprecedented efficiency and functionality. ML algorithms can aid in designing intricate PICs by swiftly exploring a vast design space and identifying optimal configurations [172]. In addition, AI-driven monitoring and control systems can dynamically adjust parameters in real time, ensuring optimal performance even in varying environmental conditions. This fusion of AI and integrated photonics holds the promise of accelerating the development of next-generation photonic technologies, empowering faster data transmission, improved energy efficiency, and enhanced functionality across various applications, from telecommunications to sensing and computing [56; 173]. Artificial neural networks (ANNs) offer a robust approach to photonic design by establishing an implicit relationship between input parameters (such as geometry and materials) and output optical responses, emulating the nonlinear nerve conduction in the human body [174; 175; 176]. Well-trained ANNs streamline the complex and time-consuming design process, which typically depends on numerical simulations and optimization. ANN models serve two primary functions in photonic design: forward prediction and inverse design. The forward prediction network estimates optical responses from geometric/material parameters, acting as a substitute for comprehensive wave simulations. The inverse design network efficiently identifies the optimal structure based on given optical responses, addressing a critical and challenging aspect of the design process. A key benefit of ANN  
Figure 7: Graphical illustration of the deposition process for the SiO\({}_{2}\): TiO\({}_{2}\) WG film [164]. models is their high processing speed, which is particularly advantageous in situations requiring rapid calculations. As demonstrated by the example of a meta-atom spectrum, a forward prediction model can generate this result in a mere few milliseconds. This is a notable improvement over the processing times typically associated with traditional full-wave simulation methods, such as finite element (FEM) and finite difference time domain (FDTD) approaches [177]. Additionally, ANN models maintain accuracy comparable to rigorous simulations, with mean squared loss of spectrum prediction typically ranging from \(10^{-3}\) to \(10^{-5}\)[178]. Furthermore, ANNs can elucidate hitherto unanticipated and non-unique relationships between physical structures and optical responses. This may prompt researchers to devise entirely novel classes of structures. [PERSON] et al. presented a practical ANN design framework specifically tailored for devices that serve as fundamental components in PICs [172]. The design methodology is presented in Figure 8a.