Micro-optics assembly for fast axis collimation by means of convolutional neural network

The manufacture of high-power diode laser systems highly depends on the quality of the collimation of semiconductor laser diodes. The collimation process is conducted by precise alignment of micro-optics, where the first and the most critical step is the placement of the fast axis collimator (FAC) lens. The sub-micron positioning of the FAC lens is conventionally conducted with an active alignment strategy by monitoring changes in the laser beam profile with an external sensitive camera. The optimization of the beam profile characteristics is controlled by a specifically programmed motorized robotic aligner. However, active alignment, a very accurate method, often results in a higher cycle time than the passive approach, where the lens is placed in a pre-measured position. Here, we developed a new active approach, without closed loop control to position the micro-optics, that relies on the use of a pretrained convolutional network (CNN). We trained and evaluated three CNNs that can predict the optimal lens position using the single camera image of the laser beam. We predict that implementation of the best performing CNN-based model would lead to a decrease in alignment time from tens of seconds to hundreds of milliseconds and will be broadly applied in a high-volume manufacturing environment.