LLNL researchers has developed designs to augment WBG/UWBG-based OALVs to improve their power handling capability under CW operational environments.  These designs include:

The approach is to build a high voltage insulator consisting of two materials:  Poly-Ether-Ether-Ketone (“PEEK”) and Machinable Ceramic (“MACOR”).  PEEK has a high stress tolerance but cannot withstand high temperatures, while MACOR has high heat tolerance but is difficult to machine and can be brittle.  MACOR is used for the plasma-facing surface, while PEEK will handle the stresses and high…

Design and construction of a photoconductive switch requires a diamond photoconductor illuminated by light of a certain excitation wavelength.  The diamond material is specifically doped with substitutional nitrogen, which act as a source of electrons.  The device architecture allows maximum light entering the aperture.  The top and bottom electrodes are made of ultra wide band gap (UWBG)…

The approach is to use a custom-designed frustrum and attach it to the optical fiber that connects to the PCSS.  Light from the fiber enters the frustrum, spreads out, and enters the PCSS.  Any unabsorbed light re-enters the frustrum and, because of its geometry, reflects back into the PCSS itself with only a negligible fraction escaping from the fiber.  The shape of the novel frustrum is…

LLNL’s approach is to use their patented Photoconductive Charge Trapping Apparatus (U.S. Patent No. 11,366,401) as the active switch needed to discharge voltage across a vacuum gap in a particle accelerator, like the one described in their other patent (U.S. Patent No.

Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.

Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…

Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…

The Optical Transconductance Varistor (OTV, formerly Opticondistor) overcomes depletion region voltage limitations by optically exciting wide bandgap materials in a compact package. A 100μm thick crystal could have the capability approaching 40kV and would replace numerous equivalent junction devices. Thus, unlike present junction transistors or diodes, this wide bandgap device can be stacked…