Multimode interference reflectors and output tuning using neural networks

Power splitting is usually accomplished in photonic integrated circuits through multimode interference devices. A compact form of such structures is the multimode interference reflector, which enables efficient light manipulation and wavelength selection. Being able to precisely tune the output characteristics of multimode interference reflectors is of paramount importance for various applications in communication systems and signal processing. Conventional methods for output tuning often rely on complex design iterations and simulations, hindering their scalability and adaptability. This research explores a novel approach to tune multimode interference reflectors using deep neural networks. By leveraging the learning capabilities of neural networks, a framework to accurately model the intricate relationships between the input parameters and the output responses of multimode interference reflector devices is being explored. A representation of a matrix of inference reflectors is considered. Then, a dataset is generated from rigorous simulations to train a neural network to predict the multimode interference reflector configuration under diverse operating conditions. A Generative Adversarial Network (GAN) is being optimized to tune the reflection characteristics of multimode interference reflectors to meet desired specifications, such as signal routing requirements and power division ratio at the output. The proposed method will significantly reduce the design cycle time, offering a substantial advantage in rapid prototyping and deployment of multimode interference reflector based photonic circuits, and showcases the potential of using neural networks for tuning these devices, presenting a transformative and data-driven approach to optimize the performance of photonic integrated circuits.