A deep learning approach for high-resolution and enhanced efficiency in photonic power dividers

Abstract

With recent developments, nanophotonics has emerged as a field of intense interest offering improvements in the performance of photonic integrated circuits (PICs). By significantly increasing data rates, transmission speed, and bandwidth and reducing both transmission loss and energy consumption, nanophotonic technologies present significant advantages that could redefine the efficiency and capabilities of PICs. In this study, the design of photonic crystal power dividers is addressed using a two-stage deep learning strategy with Deep Convolutional Generative Adversarial Networks (DCGANs). The study primarily aims for high-resolution designs compared to the existing methods. This approach expands the solution set, allowing for simulation results that more closely align with real-world performance while achieving higher transmission efficiency and output rates within practical computational limits. Previous studies were limited by low-resolution designs and the necessity for extensive optimization processes. Using the DCGAN architecture, high-resolution patterns (200 × 200 pixels) with transmission efficiencies exceeding 95% are achieved. This approach highlights the effectiveness of deep learning in discovering complex nonlinear relationships, successfully producing power dividers capable of arbitrary energy distributions. The innovative method positions the role of deep learning in advancing photonic devices and emphasizes their potential impact on neurocomputing and future hardware development.