Machine Learning-Assisted Design and Optimization of a Broadband, Low-Loss Adiabatic Optical Switch

The demand for faster and more efficient optical communication systems has driven significant advancements in integrated photonic technologies, with optical switches playing a pivotal role in high-speed, low-latency data transmission. In this work, we introduce a novel design for an adiabatic optical switch based on the thermo-optic effect using silicon-on-insulator (SOI) technology. The approach relies on slow optical signal evolution, minimizing power dissipation and addressing challenges of traditional optical switches. Machine learning (ML) techniques were employed to optimize waveguide designs, ensuring polarization-independent (PI) and single-mode (SM) conditions. The proposed design achieves low-loss and high-performance operation across a broad wavelength range (1500–1600 nm). We demonstrate the effectiveness of a Y-junction adiabatic switch, with a tapered waveguide structure, and further enhance its performance by employing thermo-optic effects in hydrogenated amorphous silicon (a-Si:H). Our simulations reveal high extinction ratios (ERs) exceeding 30 dB for TE mode and 15 dB for TM mode, alongside significant improvements in coupling efficiency and reduced insertion loss. This design offers a promising solution for integrating efficient, low-energy optical switches into large-scale photonic circuits, making it suitable for next-generation communication and high-performance computing systems.