A passively switchable erbium-doped fiber laser based on alcohol as the saturable absorber (SA) has been demonstrated. The SA is prepared by filling the gap between two optical patch cords with alcohol to form a sandwich structure. The modulation depth of the alcohol–SA is measured to be 6.4%. By appropriately adjusting the pump power and the polarization state in the cavity, three kinds of mode-locked pulse patterns can be achieved and switched, including bright pulse, bright/dark soliton pair, and dark pulse. These different soliton emissions all operate at the fundamental frequency state, with a repetition rate of 20.05 MHz and a central wavelength of ∼1563 nm. To the best of our knowledge, this is the first demonstration of a switchable soliton fiber laser using alcohol as the SA. The experimental results further indicate that organic liquid-like alcohol has great potential for constructing ultrafast lasers.

光学元件的损伤在高功率激光系统的终端光学组件中较为普遍且对激光系统的正常运行有重大影响。为提高元件的使用寿命和保证激光光路正常运行，首先要做的是检测和判断出损伤出现的位置、大小、类型。在线检测中终端光学损伤检测装置是一种重要的方法，它能够直接、实时地对元件的损伤情况进行成像并分析，另外还有一种间接获取损伤图像的方式，即用衍射环检测损伤，通过相关公式求出损伤点的大小和位置。针对更小的损伤的检测，深度学习这一工具能够处理大量数据，是目前研究该问题不可或缺的一类方法，它能够减少人工，并提高效率和准确率。修复损伤的主要方式是快速熔融缓解，即二氧化碳激光熔融损伤区，该方法是目前最常见、最有效的修复方式。对损伤问题处理的前提和关键在于精确定位更小的损伤点并分类不同类型的损伤，以便确定后续修复步骤。损伤的检测和修复是光学循环回路策略的重要部分，传统方法有一定的局限性。近些年，受到深度学习在图像处理和目标识别领域的优势的影响，未来会有越来越多深度学习的方法能够被用在与损伤检测相关的研究上。这对高功率激光系统长期稳定运行和正常发展有重要意义和作用。

A spectrum series learning-based model is presented for mode-locked fiber laser state searching and switching. The mode-locked operation search policy is obtained by our proposed algorithm that combines deep reinforcement learning and long short-term memory networks. Numerical simulations show that the dynamic features of the laser cavity can be obtained from spectrum series. Compared with the traditional evolutionary search algorithm that only uses the current state, this model greatly improves the efficiency of the mode-locked search. The switch of the mode-locked state is realized by a predictive neural network that controls the pump power. In the experiments, the proposed algorithm uses an average of only 690 ms to obtain a stable mode-locked state, which is one order of magnitude less than that of the traditional method. The maximum number of search steps in the algorithm is 47 in the 16°C–30°C temperature environment. The pump power prediction error is less than 2 mW, which ensures precise laser locking on multiple operating states. This proposed technique paves the way for a variety of optical systems that require fast and robust control.

A direct prejudgement strategy that takes the diffraction ring as the analysis target is put forward to predict hot images induced by defects of tens of microns in the main amplifier section of high power laser systems. Analysis of hot-image formation process shows that the hot image can be precisely calculated with the extracted intensity oscillation of the diffraction ring on the front surface of the nonlinear plate. The gradient direction matching (GDM) method is adopted to detect diffraction rings. Recognition of simulated diffraction rings shows that it is feasible to directly prejudge hot images induced by those closely spaced defects and the defects that are far apart from each other. Image compression and cluster analysis are utilized to optimize the performance of the GDM method in recognizing actually collected diffraction images. Results show that hot images induced by defects of tens of microns can be directly prejudged without redundant information.