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Adaptive Laser Beam Engineering with Coherent Beam Combining for Efficient Power Delivery

Khushboo Soni, S. Thirumugam, John Rozario Jegaraj, Nithyanadan Kanagaraj

TL;DR

This work tackles the challenge of delivering high-power laser energy with precise control of beam profiles by introducing an adaptive coherent beam combining (CBC) framework. It unifies three capabilities—sequential steering for dynamic patterns, adaptive static masking for predefined shapes, and fast dynamic sequencing through pre-computed phase states—under a single phase-control platform, with optimization driven by the Adagrad algorithm. The approach models a tiled-aperture CBC system, employs a PITR-based performance metric, and uses a composite merit $J(M)$ to guide mask-based shaping into rings, rectangles, and triangles while suppressing center leakage. Across simulations up to $N=217$ beams, the method demonstrates high power concentration and uniformity in complex far-field patterns, establishing a scalable, programmable path toward reusable, non-mechanical beam control for manufacturing, optical manipulation, and directed-energy applications, with experimental validation planned.

Abstract

High-power laser technologies are essential in precision manufacturing, defense, and scientific research, where accurate control of the beam profile is paramount. Although several beam-shaping methods exist, they often face implementation and scalability challenges. To address these limitations, we introduce a comprehensive and versatile framework for on-demand beam engineering through coherent beam combining (CBC) systems to precisely craft far-field intensity distributions. The proposed approach integrates limitless key capabilities: (i) dynamic beam shaping through sequential steering, (ii) structured static beam shaping allowing the direct formation of target-defined profiles, and (iii) high-speed dynamic beam sequencing without mechanical movement. Thus, the proposed approach could be a potential one-stop solution to meet wide manufacturing requirements. Rapid reconfiguration is achieved through optimized phase control of the CBC channels, supported by a deep-learning-inspired optimization algorithm. This unified CBC framework significantly improves beam uniformity, power delivery efficiency, and scalability compared to conventional techniques, thus establishing a robust platform for next-generation laser systems in industrial manufacturing, materials processing, and directed-energy systems.

Adaptive Laser Beam Engineering with Coherent Beam Combining for Efficient Power Delivery

TL;DR

This work tackles the challenge of delivering high-power laser energy with precise control of beam profiles by introducing an adaptive coherent beam combining (CBC) framework. It unifies three capabilities—sequential steering for dynamic patterns, adaptive static masking for predefined shapes, and fast dynamic sequencing through pre-computed phase states—under a single phase-control platform, with optimization driven by the Adagrad algorithm. The approach models a tiled-aperture CBC system, employs a PITR-based performance metric, and uses a composite merit to guide mask-based shaping into rings, rectangles, and triangles while suppressing center leakage. Across simulations up to beams, the method demonstrates high power concentration and uniformity in complex far-field patterns, establishing a scalable, programmable path toward reusable, non-mechanical beam control for manufacturing, optical manipulation, and directed-energy applications, with experimental validation planned.

Abstract

High-power laser technologies are essential in precision manufacturing, defense, and scientific research, where accurate control of the beam profile is paramount. Although several beam-shaping methods exist, they often face implementation and scalability challenges. To address these limitations, we introduce a comprehensive and versatile framework for on-demand beam engineering through coherent beam combining (CBC) systems to precisely craft far-field intensity distributions. The proposed approach integrates limitless key capabilities: (i) dynamic beam shaping through sequential steering, (ii) structured static beam shaping allowing the direct formation of target-defined profiles, and (iii) high-speed dynamic beam sequencing without mechanical movement. Thus, the proposed approach could be a potential one-stop solution to meet wide manufacturing requirements. Rapid reconfiguration is achieved through optimized phase control of the CBC channels, supported by a deep-learning-inspired optimization algorithm. This unified CBC framework significantly improves beam uniformity, power delivery efficiency, and scalability compared to conventional techniques, thus establishing a robust platform for next-generation laser systems in industrial manufacturing, materials processing, and directed-energy systems.
Paper Structure (18 sections, 21 equations, 8 figures)

This paper contains 18 sections, 21 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Simulation
  3. Numerical modeling of beam propagation
  4. Beam combining evaluation methodology
  5. Numerical simulation setup
  6. Algorithm—Adagrad Optimizer
  7. Laser beam engineering through CBC
  8. Beam Steering and dynamic beam shaping via sequential steering
  9. Adaptive static beam shaping
  10. Far-field model and per-pixel power
  11. Target mask
  12. Power-in-target region (PITR)
  13. Uniformity
  14. Center leakage (ring beams)
  15. Composite merit function
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Beam steering at different locations: (a--c) with side lobes and (d--f) without side lobes. (a) & (d) correspond to steering along the X-axis, (b) & (e) along the Y-axis, and (c) & (f) along the diagonal.
  • Figure 2: CBC convergence analysis using Adagrad optimization at different pixel positions for varying numbers of beams: (a) 7 beams, (b) 19 beams, (c) 37 beams, (d) 61 beams, (e) 91 beams, (f) 127 beams, (g) 169 beams, and (h) 217 beams. The bar heights represent normalized PIB values for each pixel position.
  • Figure 3: Formation and evaluation of the far-field “UFO” beam shape using sequential beam steering (a) Pixel grid showing the target “UFO” shape defined by selected beam positions. (b) Far-field intensity profile obtained with equal dwell time for all pixels, resulting in a non-uniform distribution. (c) Far-field intensity profile obtained with optimized (unequal) dwell times calculated from pixel gains, yielding nearly uniform intensity distribution across the “UFO” shape. (d) Quantitative analysis of pixel metrics: normalized gain, dwell time based on total time scanning method, dwell time based on weighted method, and corresponding weight factors for all selected pixels.
  • Figure 4: Dynamic beam shaping through sequential steering using stored phase maps and the dwell-time concept to achieve near-equal power distribution across all target patterns.
  • Figure 5: CBC convergence analysis using the Adagrad algorithm for rectangle shape: (a–d): Optimized far-field intensity profiles for 61, 91, 127, and 217 beams, (e): Convergence plots (iteration vs power-in-rectangle and uniformity) for different beams
  • ...and 3 more figures