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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. Real-Time Validation of Machine Intelligence Controlling Unmanned Vehicle Autonomous Operations

    SBC: XL SCIENTIFIC LLC            Topic: N18BT032

    To realize the full potential of autonomous systems, it is imperative that they behave safely, correctly, ethically, and legally. Providing these assurances through offline verification alone is insufficient, due to the complex and changing nature of autonomous systems. Online monitoring and corrective actions are necessary to account for uncertainties, and to increase trust between a human superv ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Collective Meta-Reasoning Runtime Assurance of Machine Intelligence for Unmanned Autonomous Vehicles

    SBC: BARRON ASSOCIATES, INC.            Topic: N18BT032

    Barron Associates has teamed with a prominent researcher in the field of formal verification of cyber physical systems to propose a new paradigm in runtime assurance for complex autonomous systems controlled and operated by artificial machine intelligence. A two-stage approach is considered in which formal verification processes are first performed offline at algorithm design time. Online, during ...

    STTR Phase I 2019 Department of DefenseNavy
  3. Computerized Robotic Delayering and Polishing System

    SBC: SPECTRAL ENERGIES LLC            Topic: DMEA18B001

    The proposed research and technical objectives in this project deal with computerized automatic delayering and polishing system that would be applicable to both commercial and government semiconductor device research and development with applications including Failure Analysis (FA), Fault Isolation (FI), and Reverse Engineering (RE) of semiconductor microelectronic devices. This project could hel ...

    STTR Phase I 2019 Department of DefenseDefense Microelectronics Activity
  4. Automated In-situ Large-area De-processing of ICs with High Throughput

    SBC: MICRONET SOLUTIONS INC.            Topic: DMEA18B001

    The objective of this proposal is to demonstrate the feasibility of producing an automated delayering and imaging system with end point detection, material density detection with built in neural network error correction. This process, coined fast Automated Delayering-Image Capture System (ADICS) leverages off of the existing Pix2Net which is a proven automated imaging 3D microchip reconstruction ...

    STTR Phase I 2019 Department of DefenseDefense Microelectronics Activity
  5. Process to Mitigate Catastrophic Optical Damage to Quantum Cascade Lasers

    SBC: IRGLARE LLC            Topic: N19AT004

    The development of a catastrophic optical damage model for quantum cascade lasers describing instantaneous laser damage at high optical power levels is proposed. The model will be validated by comparison to experimental data. Based on obtained results, changes to laser design and laser fabrication resulting in an increased damage threshold will be implemented. The work will ultimately result into ...

    STTR Phase I 2019 Department of DefenseNavy
  6. Power-Dense Electrical Rotating Machines for Propulsion and Power Generation

    SBC: CONTINUOUS SOLUTIONS LLC            Topic: N19AT007

    The primary objective is to develop electric machine/drive topologies and power architectures that achieve the power densities required for 50% more power without the increase in weight or space requirements. In addition to PMSM-based designs, two new machine topologies will be considered. The first is a trapped flux coreless (TFC) machine that utilizes superconducting pucks made of YBCO to produc ...

    STTR Phase I 2019 Department of DefenseNavy
  7. Power Dense Turbo-Compression Cooling Driven by Waste Heat

    SBC: MANTEL TECHNOLOGIES, INC.            Topic: N19AT013

    The U.S. Navy seeks methods to improve the fuel economy of marine diesel engines through utilization of waste heat. Low temperature engine jacket water, lubrication oil, and aftercooler air are largely untapped streams of thermal energy on these ships, but their utilization circumvents many operation challenges associated with exhaust gases. For example, variable and high exhaust gas temperatures ...

    STTR Phase I 2019 Department of DefenseNavy
  8. High Speed Spinning Scroll Expander (HiSSSE)- Organic Rankine Cycle for Increased Naval Ship Power Density and Fuel Efficiency

    SBC: Air Squared, Inc.            Topic: N19AT013

    Waste heat from Naval diesel generators provides significant opportunity to introduce organic Rankine cycles (ORC) to increase their fuel efficiency. The objective of the proposed effort is to design and demonstrate a high-speed, spinning scroll expander (HiSSSE) ORC as a power dense waste heat recovery system for diesel generators on ships. The system will leverage Air Squared’s spinning scroll ...

    STTR Phase I 2019 Department of DefenseNavy
  9. Compact Waste Heat Recovery Power Generation System

    SBC: SPECTRAL ENERGIES LLC            Topic: N19AT013

    The STTR topic N19A-T013 seeks innovative technology to improve the power density and efficiency of propulsion and power generation devices. To address this challenge, Spectral Energies in collaboration with its academic partner Dr. Rory Roberts at Wright State University proposes to develop a compact heat recovery system based on a supercritical CO2 based Rankin Cycle. At the end of the STTR prog ...

    STTR Phase I 2019 Department of DefenseNavy
  10. Predictive Graph Convolutional Networks- 19-008

    SBC: METRON INCORPORATED            Topic: N19AT017

    Metron and Northeastern University propose to design, develop, and validate a proof-of-concept predictive Graph Convolutional Network (GCN) capability using open source Reddit and GDELT data. We propose: (1) to extract and preprocess open-source Reddit and GDELT data, (2) to design a predictive graph convolutional neural network model, (3) to implement and train that model, and (4) to validate the ...

    STTR Phase I 2019 Department of DefenseNavy
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