Ziv Ron, Dikopoltsev Alex, Zahavy Tom, Rubinstein Ittai, Sidorenko Pavel, Cohen Oren, Segev Mordechai
Opt Express. 2020 Mar 2;28(5):7528-7538. doi: 10.1364/OE.383217.
We propose a simple all-in-line single-shot scheme for diagnostics of ultrashort laser pulses, consisting of a multi-mode fiber, a nonlinear crystal and a camera. The system records a 2D spatial intensity pattern, from which the pulse shape (amplitude and phase) are recovered, through a fast Deep Learning algorithm. We explore this scheme in simulations and demonstrate the recovery of ultrashort pulses, robustness to noise in measurements and to inaccuracies in the parameters of the system components. Our technique mitigates the need for commonly used iterative optimization reconstruction methods, which are usually slow and hampered by the presence of noise. These features make our concept system advantageous for real time probing of ultrafast processes and noisy conditions. Moreover, this work exemplifies that using deep learning we can unlock new types of systems for pulse recovery.
我们提出了一种用于超短激光脉冲诊断的简单全在线单次测量方案，该方案由多模光纤、非线性晶体和相机组成。该系统记录二维空间强度图案，通过快速深度学习算法从中恢复脉冲形状（幅度和相位）。我们在模拟中探索了该方案，并展示了超短脉冲的恢复、对测量噪声和系统组件参数不准确的鲁棒性。我们的技术减少了对常用迭代优化重建方法的需求，这些方法通常速度较慢且受噪声影响。这些特性使我们的概念系统在超快过程和噪声条件的实时探测方面具有优势。此外，这项工作表明，利用深度学习我们可以解锁新型的脉冲恢复系统。
