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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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.

  1. Quantum Optical Semiconductor Chips and its Application to Quantum Communication

    SBC: Maxxen Group, LLC            Topic: N20AT005

    Current Superconducting Quantum Interference Device (SQUID) technology is capable of quantum computing, however, its application is limited due to its large size, low-temperature refrigeration requirement, and high cost.  Quantum optical semiconductor-scale chip technology is promising but not commercially available yet due to the multiple challenges to overcome. Using quantum photonic technology ...

    STTR Phase I 2020 Department of DefenseNavy
  2. Intelligent & Secure Probing for Embedded Condition and Threat Monitoring (INSPECT-M)

    SBC: Luna Innovations Incorporated            Topic: N20AT011

    The United States Navy is currently developing Condition Based Maintenance Plus (CBM+) concepts and technologies in order to improve the readiness and availability of Department of Defense assets by maximizing efficiency and reducing the life-cycle maintenance costs through data-driven decisions. A secure CBM+ sensor node has the potential to reduce the number of machine overhauls, shorten the tim ...

    STTR Phase I 2020 Department of DefenseNavy
  3. Intelligent Additive Manufacturing- Metals

    SBC: R3 DIGITAL SCIENCES INC            Topic: N20AT018

    The Navy desires an Intelligent Additive Manufacturing (IAM) system for metal Laser Powder Bed Fusion (LPBF) that can incorporate AI to provide real-time adaptive monitoring and control of the LPBF process and produce defect-free parts while maintaining or reducing part build times. To provide this, our team will develop Open-IAM.  The concept will consist of a controllable open architecture LPBF ...

    STTR Phase I 2020 Department of DefenseNavy
  4. Timing and Harmonic AI-based Waveform Error Detection (THAWED)

    SBC: Expedition Technology, Inc.            Topic: N20AT025

    The physics generating timing spurs in a high-speed, low-bit depth analog-to-digital converter will be modeled in a machine learning framework to enable both the prediction of spurs and adaptive removal using an enhancement neural network. The algorithms will be developed in software and optimized for low-latency operation in a digital (FPGA or similar) framework. High-speed implementation of ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Improved Identification of the function of Novel and Partially Occluded Laboratory Equipment.

    SBC: Novateur Research Solutions, LLC            Topic: DTRA19B002

    This STTR Phase 1 project proposes development of a statistical relational learning framework for identification of the function of laboratory equipment from imagery. The proposed framework uses semantic reasoning to incorporate evidence from multiple classifiers and feature extractors, domain knowledge, and scene context for scene understanding and labeling. The Phase I effort will focus on imple ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  6. Self-fragmenting Structural Reactive Materials (SF-SRM) for High Combustion Efficiency

    SBC: Matsys Incorporated            Topic: DTRA16A002

    MATSYS proposes to develop, test and evaluate a scalable metal-based reactive structural material that will self-fragment to micron or sub-micron scale fuel particles when subjected to explosive shock loading, resulting in significantly enhanced metal combustion efficiency. Use of reactive material casings offers the potential for several-fold increases in blast and overpressure by generating rapi ...

    STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
  7. Rapid Development of Weapon Payloads via Additive Manufacturing

    SBC: Matsys Incorporated            Topic: DTRA16A001

    MATSYS proposes to adapt emerging additive manufacturing techniques (so-called 3-D Printing) for use with reactive structural materials and demonstrate this capability to rapidly fabricate reactive case. Our concept incorporates two major manufacturing steps: 3D printing of green compacts from pure Al or Al-based reactive powder blend; and Microwave (MW) sintering of green compacts into net-shaped ...

    STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
  8. Fully Metallic Self-Fragmenting Structural Reactive Materials Using Composites and Alloys Comprised of Aluminum, Lithium, and Magnesium

    SBC: Adranos Energetics LLC            Topic: DTRA16A002

    While aluminum casing materials provide some enhanced performance and thermal loading to explosive ordinance, their overall effectiveness is highly limited by incomplete combustion and long residence times. In order to reduce these problems, the casing material must be designed to facilitate rapid fragmentation through either specialized casing geometries or greatly refined initial particle sizes. ...

    STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency
  9. Indicator Yarns to Detect Degradation in Nylon Webbing

    SBC: Nanosonic Inc.            Topic: N19BT032

    Nylon webbing is used for the load bearing elements in the parachute system - harnesses and risers, and degrades with use, depending on environmental conditions and frequency of use. The environmental factor that causes the most degradation of polymers in webbing is ultraviolet (UV) irradiation; this factor, combined with repeated mechanical strain, can accelerate degradation to a point where the ...

    STTR Phase I 2020 Department of DefenseNavy
  10. Machine Learning Tools to Optimize Metal Additive Manufacturing Process Parameters to Enhance Fatigue Performance of Aircraft Components

    SBC: TECHNICAL DATA ANALYSIS, INC.            Topic: N20AT002

    In this SBIR effort, TDA and its team partners propose to develop a comprehensive toolset based on an Integrated Computational Material Engineering (ICME) framework using Machine Learning (ML) and Artificial Intelligence (AI) algorithms to predict mechanical performance and fatigue life in additively manufactured (AM) metallic components. The toolset addresses fatigue contributing factors, includi ...

    STTR Phase I 2020 Department of DefenseNavy
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