High-Precision Spatial Filtering with AI-driven control and multicolor laser testing for interferometric application

This study presents a novel automated spatial filtering system designed for high-precision alignment and operation across multiple laser wavelengths. Spatial filtering has been a cornerstone of optical research and applications, playing a critical role in beam quality improvement, noise reduction, and the mitigation of back reflections. Building upon the advancements in pinhole designs, external-cavity configurations, and beam alignment methods, our system integrates a laser diode, a $10\mu m$ pinhole, servomotors for micrometric adjustments, and a CCD camera for real-time feedback. Robust testing was conducted with red, green, and blue lasers (635, 520, and 405 nm) to ensure consistent performance across varying wavelengths. The control mechanism employs a two-stage approach: coarse alignment using a back-propagation artificial neural network for efficient displacement estimation, followed by fine-tuning with an enhanced proportional–integral–derivative (PID) controller to achieve sub-micrometric precision. This hybrid approach addresses the limitations of traditional manual methods and standalone controllers, significantly reducing alignment time and improving accuracy. The software architecture, implemented in Python, LabVIEW, and MySQL, ensures cross-platform compatibility and modular integration into embedded systems. This automated solution is particularly suitable for holographic and interferometric experiments that demand continuous and precise spatial filtering. By achieving high reproducibility and adaptability across multiple wavelengths, this advancement offers a scalable and reliable solution to enhance experimental precision in optical research and engineering applications.