Deep Learning Control of Femtosecond Laser Nano-Ridge Processing

The patent implements a closed-loop feedback system where deep learning models analyze processing results in real-time and dynamically adjust laser parameters (power, speed, frequency) to optimize processing speed and quality. The system continuously learns from processing outcomes to improve future performance, transforming traditional open-loop laser processing into an intelligent adaptive system.

The patent utilizes deep learning to intelligently optimize multiple laser processing parameters simultaneously, including pulse duration, repetition frequency, power density, and scanning speed. By dynamically adjusting these parameters based on learned patterns and real-time feedback, the system achieves both high processing speed and high intelligence, resolving the contradiction between productivity and automation extent.

The patent employs periodic femtosecond laser pulses with optimized repetition frequencies to achieve both ultra-fine precision and high speed. By using pulsed rather than continuous laser delivery, the system allows heat dissipation between pulses (preventing damage) while maintaining high average power for fast processing. The deep learning system optimizes the pulse frequency and duty cycle to balance precision and speed.

The patent implements dynamic adjustment of laser parameters during processing based on real-time feedback from deep learning analysis. The system can adaptively change pulse duration, power, and scanning speed during different stages of processing to maintain optimal precision while maximizing overall processing speed. This dynamic control resolves the static trade-off between precision and speed.

The patent implements a self-optimizing processing system where deep learning models automatically analyze material properties, select optimal processing parameters, and adjust settings in real-time without extensive human intervention. The system self-adapts to different materials and structures by learning from processing outcomes, reducing the complexity burden on operators while maintaining high chip performance through precise control.

The patent develops a universal deep learning-based control system that can handle multiple material types, structures, and processing requirements through a single integrated platform. The system uses transfer learning and generalizable models to adapt to different 5G chip materials and designs without requiring completely separate processing configurations, thereby reducing overall processing complexity while maintaining high performance across diverse applications.