Infrared cameras could serve automotive applications by delivering breakthrough perception systems for both in-cabin passengers monitoring and car surrounding. However, low-cost and high-throughput manufacturing methods are essential to sustain the growth in thermal imaging markets for automotive applications, and for other close-to-consumer applications which have a fast growth potential. Fast low cost infrared lenses suitable for microbolometers are currently already sold by companies like Umicore, Lightpath, FLIR… They are either made of a single inverse meniscus Chalcogenide glass or of two Silicon optics. In this paper, we explore hybrid systems with a large field of view around 40° combining Chalcogenide and Silicon in order to take advantage of both materials. Both are compatible with wafer-level process. Silicon optics can be manufactured by photolithography process and are expected to be more cost-effective than Chalcogenide ones. However they are constrained in shape and sag height. On the other hand, Chalcogenide optics can be collectively molded and could have more free shapes. They are thus more suitable to reach high-demanding performance. So hybrid designs could be seen as a compromise between cost and performance. In this paper, we show that fast lenses with diameter constraints to few millimeters to make affordable wafer-level process lead to small size detectors. As a consequence, the pixel pitch reduction of microbolometers is a key point to maintain a good resolution. Finally, strategies to improve the production yield of hybrid lenses are explored.
Due to their short focal lengths, FAC lenses significantly influence the performance of high-power diode laser systems. In addition to the shape, coating and surface quality, high demands are placed on the assembly accuracy for these microoptical components. In order to optimally align and position the lenses despite varying properties (e.g. focal length), active alignment strategies are used. The automation of the active alignment process for production offers enormous potential. Compared to manual processes, the reproducibility and accuracy of the alignment is increased. For the automation of the active alignment process, a deep understanding of the system behaviour is necessary. To control a diversity of variants cost-effectively and robust, new approaches must be taken into account. Concepts of AI or machine learning are great for this kind of generalization and adoption and they have many advantages for the active alignment of systems like DOEs or free-form-optics, with a complex system behaviour. In this publication, we want to compare the performance of a classically model-based algorithm and a machine learning approach for the automated active alignment of FAC-lenses. The model-based algorithm uses a physical model of the metrology system (including the FAC to be aligned) to estimate a misalignment in 4-DOF. The machine learning algorithm consist of a deep neuronal network which was trained with image data.
Automated, ultra-precise packaging strategies reduce production time and costs while increasing yield, quantity, and precision, making them one of the main research and development questions in the field of production technology. Fraunhofer IPT develops sensor-guided assembly solutions for packaging and testing of optical and non-optical components to meet the demand. In this paper, we present a prototypical process for the automated, ultra-precise passive alignment using the assembly of a diamond engraving tool as an example. The challenge is to place a diamond measuring three millimetres in its largest dimension into a groove of similar size and to position the tip of the diamond within tolerances of a few micrometres and arcminutes. This six dimensional assembly problem is tackled by feeding live camera data to an image processing algorithm and by aligning the diamond using Fraunhofer IPT’s ultra-precise micromanipulator, collectively forming an automated, closed-loop assembly process. Thus, a fully automated packaging process with very high accuracy and reliability is proven to be technically possible.
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