L3Harris (United States)

The Roman Space Telescope is a three mirror anastigmat design with a 2.4 m primary mirror, which will provide science information on dark energy and exoplanets via two science instruments. The Optical Telescope Assembly (OTA) was aligned at ambient temperature in a vacuum chamber. Following a vibration test, the OTA’s optical performance, wavefront error, and pupil alignment, were tested at cold operational temperatures in a thermal vacuum chamber. Both test campaigns were performed in double-pass configuration, employing interferometry, Focus Diverse Phase Retrieval, and Shack-Hartman Wavefront Sensing test techniques. Alignment adjustments during the tests were made to the actuated Secondary Mirror, and Fold Mirror 1 in the Wide Field Instrument channel. Stability of the optical telescope assembly (OTA) is a critical need for Roman. For the TVAC test campaign, L3Harris and NASA developed a novel test with the objective of measuring the optical response of key parts of the OTA in the presence of small amplitude thermal stimuli; the Sinusoidal Thermoelastic Distortion Test (STDT). The basic concept is to use the flight hardware’s own thermal control system to generate repeated thermal disturbances while continuously monitoring the optical response. The telescope’s predicted On-Orbit, End of Life quasi static optical wave front error performance is well within the required 67.3/74.2 nm RMS for the WFI/CGI channel.

Mapping products for flight simulators require feature classification for LandUse/LandCover (LULC) and sensor simulation. The challenge lies in providing accurate material classification of imagery, while significantly reducing the analyst processing time and cost. Future quantum computers may provide sufficient speed for processing every pixel simultaneously. Image segmentation can be thought of as pixel clustering with each pixel label corresponding to a particular LULC class. We characterize a supervised quantum machine learning (QML) use case with imagery and lidar. The fast and accurate segmentation of an image into just a few LULC classes (e.g., buildings, bare earth, roads, water, vegetation) can benefit material classification applications. We classically simulate a binary classification quantum circuit as part of a quantum deep learning layer to help distinguish LULC features. We also use an ensemble of quantum neural network (QNN) machine-learning models with different solvers. We then use a linear program (LP) approximation circuit to optimally decide which model to use per pixel in a one-sided-versus-nature game-theoretic implementation. This input matrix is generated from goodness-of-fitness probability metrics. Furthermore, we perform multiclass feature decision making for our quantum algorithm using a binary comparison matrix. We process a pairwise reward matrix of prediction probability confidences to choose the pixel class. The optimal decision making from these matrices uses an LP approximation subset-summing method with quantum adders and comparators. As the size of the reward matrix increases, LP approximation approaches the performance of a classical LP. We compare classical and quantum image segmentation accuracy results with confusion matrices.

L3Harris has vertically integrated cryo-cooler production for more than 15 years. In that time the cryo-coolers products have been developed for thermal load capability between 200 – 5,000 mW to support internal and external customers. The technology focus has been to addresses micro-miniaturization and large thermal load cryo-cooler packages while promoting high capability. Recent inroads have been to promote high reliability and increase manufacturability as a domestic Cryo-cooler producer. The next step is for L3Harris Space and Sensors to commercialize our cooler product line over the next 12-24 months, as we leverage our established high-rate production capabilities and the stabilized maturity of our product designs. We are investing in our infrastructure to support the consistent output of high-quality products, ensuring that we can meet market demands swiftly and efficiently. The design maturity of our offerings reflects our commitment to reliability and excellence, having undergone extensive verification and validation.

Abstract Advanced 10 nm device delayering is a critical process for verifying and validating the circuit design layout and extracting the structures buried inside the chip. The work featured in this article takes advantage of chemically assisted focused ion beam processing with ultraviolet spectroscopy to destructively delayer integrated circuits starting from the shallow trench isolation layer, enabling high-resolution SEM imaging at each layer.

Quantum magnetometry has emerged as a promising technique for revolutionizing sensing capabilities in various fields, including autonomous underwater vehicles (AUVs). This abstract explores various methods of quantum magnetometry and their application to AUVs, focusing on its principles, challenges, and potential impacts. Quantum magnetometry leverages the principles of quantum mechanics to measure magnetic fields with high levels of sensitivity and precision. By utilizing the quantum properties of atomic systems, such as spin coherence and quantum superposition, quantum magnetometers can detect minute changes in magnetic fields, making them ideal for applications where high sensitivity is crucial. In the context of AUVs, quantum magnetometry offers several advantages over traditional sensing technologies. AUVs are deployed in diverse environments, including deep-sea exploration, marine surveillance, and underwater infrastructure inspection, where accurate detection and mapping of magnetic fields are essential for navigation, object detection, and environmental monitoring. However, traditional magnetometers often suffer from limited sensitivity, calibration requirements, susceptibility to noise, and bulky size, constraining the capabilities of AUVs. By integrating quantum magnetometers into AUVs, researchers aim to overcome these limitations and enhance their sensing capabilities. Quantum magnetometers, such as atomic vapor magnetometers (AVMs) and nitrogen vacancy (NV) centers in diamond among others, offer significant advantages compared to many traditional magnetometers. This may include heightened sensitivity that would enable AUVs to detect even weaker magnetic signals from underwater objects, geological formations, and natural phenomena with greater accuracy and reliability. Furthermore, quantum magnetometers exhibit fast response times and high spatial resolution, enabling AUVs to perform real-time mapping and localization tasks with high precision. These capabilities are particularly valuable for applications such as pipeline inspection, magnetic navigation (MagNav), and unexploded ordnance (UXO) detection and localization, where detailed mapping of magnetic anomalies is essential for decision-making and risk assessment. However, the integration of quantum magnetometers into AUVs presents several technical challenges. Miniaturization of quantum sensors without compromising performance is a significant hurdle, as AUVs require compact and lightweight payloads to maintain maneuverability and endurance. Additionally, mitigating environmental factors, such as temperature variations, pressure fluctuations, and electromagnetic interference via shielding or signal processing is crucial to ensure the reliability and stability of quantum magnetometry measurements in underwater environments. Power management can also be a challenge due to the limited capacity for onboard supplies, competing sensor needs, and mission duration requirements. Despite these challenges, recent advancements in nanofabrication, quantum optics, and signal processing techniques have paved the way for the development of compact and robust quantum magnetometers suitable for integration into AUVs. Collaborative efforts between physicists, engineers, and marine scientists have led to the successful deployment of prototype AUVs equipped with quantum magnetometry sensors in field experiments, demonstrating their potential for underwater applications. L3Harris is investigating NV center sensing technology for use in its Iver AUV to enhance multi-mission capability. To effectively operate across increasingly complex maritime environments, AUVs must be equipped with flexible, modular, and scalable technologies supporting multiple mission sets. This paper aims to introduce the reader to quantum magnetometers and their potential impact on underwater sensing applications. Moreover, to introduce the development of a novel NV center magnetometer in a low size, weight, and power (SWaP) package as a first step towards evaluation and integration onto an AUV. A future paper will provide in-depth study results. In summary, quantum magnetometry offers a potential for enhancing the sensing capabilities of AUVs, enabling them to perform complex tasks with unprecedented precision and efficiency. Continued research and development efforts are necessary to address technical challenges and optimize the integration of quantum magnetometers into AUV platforms, unlocking new opportunities for underwater exploration, monitoring, and surveillance.

Airborne LIDAR sensors can produce accurate 3D point clouds for terrain mapping at different altitudes. As the altitude increases, there is a need for larger aperture sizes to ensure the collection of sufficient photons and the preservation of spatial resolution. In the case of conical scanning optical systems, axially spinning refractive wedges can be used to cover a scan across the field of regard. Nevertheless, maintaining rotational balance for refractive wedges proves challenging, particularly at angles exceeding several degrees, due to their asymmetric moment of inertia. In contrast, a holographic optical element serves as an alternative scanning optic with a symmetric moment of inertia, effectively addressing stability concerns associated with substantial scan angles compared to refractive wedge-based scanners. Our study highlights that HOEs can accommodate a wide range of scan angles and aperture sizes without compromising volumetric constraints or stability, showcasing their effectiveness in optical scanning for LIDAR sensors.

This paper uses a track structure theory (TST) model for the calculation of 14 MeV neutron cross-section from heavy-ion data. The performance of the TST model is compared to other models in the literature. The TST model is also used to calculate heavy-ion cross sections from 14 MeV neutron data. The models are evaluated using data in the published literature. The TST model shows good agreement providing validation of the model.

NASA’s Habitable Worlds Observatory will consist of a segmented telescope and high contrast coronagraph to characterize exoplanets for habitability. Achieving this objective requires an ultra-stable telescope with wavefront stability of picometers in certain critical modes. The NASA funded Ultra-Stable Large Telescope Research and Analysis – Technology Maturation program continues to mature key component-level technologies for this new regime of “ultra-stable optical systems,” including active components like segment edge sensors, actuators and thermal hardware, passive components like low distortion mirrors and stable structures, and supporting capabilities like precision metrology. This paper will present an update to the latest results from hardware testbeds and simulations in the areas listed above. It will also contain a correction to previously published results of Ball’s Integrated Demo, which consists of a capacitive sensor and three actuators operating in closed loop.