Applications of Technology:
- High-power laser systems
- Material processing
- Medical and surgical applications
- Defense and military
- Nuclear fusion experiments
- Laser-plasma accelerators
Benefits:
- Enhanced beam stability in high-power, low-repetition rate lasers
- Reduces pointing error to a fraction of the laser beam size at the focal point
- Self-learning AI inference engine capable of adapting to system changes
- Scalable solution for complex setups
Background: High-power lasers enable precise control and high-intensity energy delivery for critical scientific, industrial, security, and medical applications. The most sensitive high-power laser systems require positional stability at focus to be a small fraction of the spot size, though current systems struggle to achieve such stability. This technology presents a machine learning approach to predict and correct laser pointing errors in real time, serving as the first robust solution for laser point stabilization.
Technology Overview: Scientists at Berkeley Lab have developed a machine learning approach to correcting laser pointing errors in real time, serving as a robust implementation of predictive control in high-power, low-repetition rate lasers for pulse-to-pulse stabilization. Key features of this technology include:
- Unprecedented stabilization accuracy: When tested on the Berkeley Lab Laser Accelerator (BELLA) Center Petawatt 1 Hz beamline, the method achieved 0.34 and 0.59 microradian RMS pointing stabilization in x and y respectively, corresponding to 0.18 and 0.32 times the beam diameter at focus. This represents a jitter reduction of 65% in x and 47% in y compared to uncontrolled beams.
- Precise pilot beam: A high-frequency pilot beam is used to detect errors, enabling preemptive adjustments of a correction mirror to compensate for predicted fluctuations
- Robust AI engine: A real-time inference engine performs pattern recognition, allowing for autonomous self-calibration and continuous self-learning to adapt to system changes.
- Fully integrated system: The controller provides synchronous data acquisition, camera interfacing, and optical alignment feedback mechanisms, improving stabilization accuracy.
Development Stage: Proven principle (TRL 3 – Analytical and experimental critical function and/or characteristic proof of concept).
Principal Investigators: Qiang Du, Dan Wang, Alessio Amodio, Curtis Berger, Anthony Gonsalves, Hai-En Tsai, Samuel Barber, Jeroen van Tilborg, Russell Wilcox, Alex Picksley, Zak Eisentraut, Neel Rajeshbhai Vora, Mahek Logantha, Qing Ji