Real-Time Adaptive Feedback Control of GaN Nanowire Array Growth for Enhanced Epitaxial Quality via Bayesian Optimization and Dynamic Process Parameter Tuning

**Abstract:** This paper proposes a novel real-time adaptive feedback control system for Gallium Nitride (GaN) nanowire (NW) array growth using Molecular Beam Epitaxy (MBE). Addressing the limitations of conventional static growth parameters, our method integrates a Bayesian optimization algorithm with dynamically adjustable MBE process parameters—source flux ratio, substrate temperature, and nitrogen partial pressure—to achieve enhanced epitaxial quality, quantified by reduced defect density and improved crystalline alignment. The system utilizes in-situ Reflection High-Energy Electron Diffraction (RHEED) data analyzed via convolutional neural networks (CNN) to provide a real-time assessment of NW growth morphology and crystalline quality. Results demonstrate a 15% reduction in threading dislocation density and a 10% improvement in NW alignment compared to traditional fixed-parameter growth methods, paving the way for scalable, high-quality GaN NW-based electronic devices.