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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Low Cost W-Band Imaging Array

    SBC: MILLIMETER WAVE SYSTEMS LLC            Topic: CBD22BT001

    Low-cost systems operating at video rates within W-band have remained elusive – especially for stand-off and remote applications. Real time video rate imaging requires parallel detection modalities that traditionally led to high costs and calibration challenges. Substantial advances in low-cost packaging and chip-level integration driven by commercial millimeter-wave applications can now be appl ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  2. Low Cost Imaging In The mm Wave Region Using Plasma Waves in High Mobility Transistor

    SBC: BRIMROSE TECHNOLOGY CORP            Topic: CBD22BT001

    In this work, we propose to develop low-cost, high sensitivity high electron mobility transistor-based W-band millimeter wave focal plane array/camera based on mature ternary III-V epitaxial materials of InAlAs on top of InP substrate. The plasma-wave detector uses well established mature technology of high electron mobility transistors which allows future integration and reduces cost. The detecto ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  3. Bounding generalization risk for Deep Neural Networks

    SBC: EULER SCIENTIFIC            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Bounding generalization risk for Deep Neural Networks

    SBC: EULER SCIENTIFIC            Topic: NGA20A001

    Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

    STTR Phase II 2022 Department of DefenseNational Geospatial-Intelligence Agency
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